rafmacalaba/datause-encoder-data huggingface dataset:
women’s participation in occupations which require work outside the home, full-time hours, and higher skills rose more where the reform had been enacted (controlling for time and location effects).

Property rights can be set out in family law as well as in land law. Here too the literature has found significant gender impacts from differences in the strength of rights to land. Secure property rights to land are fundamental to agriculture and rural economic activity. Without secure rights, the incentive to invest is diminished, as it is not clear the rewards of the investment can be enjoyed. In addition, the lack of such rights undermines the land owner’s ability to use it as collateral, leading to credit constraints. Besley (1995) demonstrates that individuals in Ghana vary their investment across plots depending on the security of their rights. Goldstein and Udry (2008) show that in a society where land rights are not secure, it is those at the top of the local hierarchy—those with the more tenured rights—that benefit the most.

A number of other empirical studies point to increased agricultural productivity as a result of women controlling larger shares of resources (Saito, Mekonnen, and Spurling 1994; Udry and others 1995; Quisumbing 1996; Besley and Ghatak 2009). More resources in women’s hands is also associated with a decline in poverty (World Bank 2001). And the benefits can transfer across generations. Duflo’s work in South Africa showed that when women received additional resources, the spending on education and food, particularly for girls, went up (Duflo 2003). Indeed, many of the conditional cash transfer programs, inspired by the results of Progresa in Mexico, now specifically target women as recipients (Skoufias and McClafferty 2001).

Beyond general principles that give everyone equal access to own property, how property rights are implemented can be important. Even when a household has secure property rights, 6
economics literature investigates the demand for insurance in a static model where the “time diversification strategy” provided by savings cannot be addressed. On the other hand, the literature on savings is based on a lifecycle consumption model where insurance is considered unavailable for consumers. There is however a few exceptions. Sakurai and Reardon (1997) explore the demand for formal drought insurance and infer the household’s willingness to pay for a hypothetical formal drought insurance. Their results generally support the hypothesis that “those who are wealthier and/or more self-insured demand less formal drought insurance” (p.1206). Using a fairly simple simulation model, Gollier (2003) shows that the ability for consumers to self-insure by accumulating buffer stock wealth, i.e., precautionary savings, induces them to significantly reduce their demand for insurance. In other words, the added value of insurance may be small when self-insurance can finance the loss. As a consequence, only liquidity constrained households would purchase a high level of coverage, while wealthier people would mostly rely on their ability to engage in inter-temporal diversification strategies. These main findings of the model can be recast in a two-period model (see Box 5). The household’s ability to build _risk-free_ savings induces it to increase its insurance coverage. The optimal insurance coverage and the optimal amount of savings are driven by the cost of insurance, the rate of return on savings, the household’s initial wealth and the risk characteristics.

# **Box 5: Insurance and savings**

Insurance and savings as substitutes or complements are investigated in a simple two period model. The agent is endowed with an initial wealth at the beginning of each period. In the second period, he faces a risk of loss. The loss is characterized by its severity _l_ (expressed in percentage of the initial wealth) and its frequency _p_ . The agent can manage this risk by purchasing insurance in the second period and/or by building savings during the first period. He maximizes the discounted expected utility of his final wealth, with his utility function exhibiting constant relative risk aversion.

where β is the rate of time preference, _r_ is the interest rate, γ is the agent’s degree of relative risk aversion and λ is the loading factor of the insurance policy.

Source: Authors, adapted from Gollier (2001).

The optimal risk-financing strategy using insurance and savings is illustrated in Figure 6 using the approximate analytical solutions provided in Box 4. They illustrate how the optimal strategy would depend on (i) the crop yield risk characteristics summarized here by the return period and the average loss per event and (ii) the farmer’s financial profile and particularly by his ability to finance unexpected adverse production shocks.

> The farm faces a loss characterized by the return period (i.e., 1 _p_ where _p_ is the annual probability of occurrence of the loss event) and the average loss per event _l_ expressed in percentage of the farmer’s wealth. The annual average loss is assumed to be one percent of

16
<!-- Start of picture text --> Broad Norm: Men and women should have equal opportunities<br>Gender Gaps in Personal Beliefs and Perceived Community Norms<br>100<br>95<br>“1185 l : ‘<br>80<br>7065 ! 8<br>55 |<br>50 I<br>a a i i i, a |<br>EAP ECA LAC MENA North America South Asia SSA<br>Region of the world<br>@ Female - Personal Beliefs @ Female - Perceived Community Norms<br>@ = Male - Personal Beliefs @ = Male - Perceived Community Norms<br>Personal beliefs are an aggregate of the percentage of the sample who agree with the statement.<br>Perceived community norms are an aggregate of the proportion of neighbors who they believe agree with the statement.<br>Aggregates are taken at the country level and then averaged by region of the world.<br><!-- End of picture text -->

<!-- Start of picture text --> Breadwinner Norm: Expenses are a man's responsibility even if his wife can help him<br>Gender Gaps in Personal Beliefs and Perceived Community Norms<br>80<br>75<br>70<br>65 t<br>60 3 e<br>55 ®<br>50 3<br>s& 4035 |<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MENA North America South Asia SSA<br>Region of the world<br>@ Female - Personal Beliefs @ Female - Perceived Community Norms<br>@ = Male - Personal Beliefs @ Male - Perceived Community Norms<br>Personal beliefs are an aggregate of the percentage of the sample who agree with the statement.<br>Perceived community norms are an aggregate of the proportion of neighbors who they believe agree with the statement.<br>Aggregates are taken at the country level and then averaged by region of the world.<br><!-- End of picture text -->

<!-- Start of picture text --> Caregiver Norm: Women's most important role is to take care of her home and children<br>Gender Gaps in Personal Beliefs and Perceived Community Norms<br>80<br>75<br>65-370 :<br>60 e ?<br>5550 !<br>45<br>s& 40<br>35<br>30 8 i<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MENA North America South Asia SSA<br>Region of the world<br>@ Female - Personal Beliefs @ Female - Perceived Community Norms<br>@ = Male - Personal Beliefs @ Male - Perceived Community Norms<br><!-- End of picture text --> Personal beliefs are an aggregate of the percentage of the sample who agree with the statement. Perceived community norms are an aggregate of the proportion of neighbors who they believe agree with the statement. Aggregates are taken at the country level and then averaged by region of the world.
Different timing of the impact of the onset of the disease could also potentially influence the pattern infection of age. In countries that were struck in an early phase of the pandemic, it came largely as a surprise. In contrast, for countries where the disease emerged at a later stage, there was greater opportunity to take preventative measures, including those to protect older people. Figure 11 shows the dates of the 100<sup>th</sup> death due to the virus in each country plotted against rates of increase by age of the COVID-19 mortality. By this metric, COVID-19 arrived later on average to the countries with flatter age-mortality curves <!-- Start of picture text --> Figure 11: Rates of Increase per Year of Age in COVID-19 Mortality<br>vs. Date of 100 th COVID-19 Death in the Country g<br>CHE<br>CAN<br>NLD<br>DNK<br>AUS<br>ESP SWE<br>ITA KORDEU<br>PRT NORHUN<br>FRA<br>JPN<br>CHL<br>ARG<br>PHL<br>BRA<br>COL MYS<br>ZAF<br>PER AFG<br>PAK<br>MEX<br>Mar1 Apr1 May1 Jun1<br>Date of 100th death<br>.15<br>.1<br>Rate of increase in COVID-19 mortality rate<br>.05<br><!-- End of picture text --> Note: See Appendix for data sources. LMICs are shown in red and HMICs in blue.

Age-specific mortality rates among those infected may also vary by age due to a wide variety of factors. Non-communicable diseases (NCDs) are more widespread in HICs. NCDs like chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity are key risk factors for mortality among COVID-19 patients (Docherty, Harrison et al. 2020). Goldberg and Reed (2020) find that the higher prevalence of obesity in high income and upper-middle income countries partially explains variation in COVID-19 death rates across countries. Ghisolfi, Almas et al. (2020) predict IFRs across countries and find that for low income countries in particular taking comorbidities into account increases their predicted fatality rates. It is not clear, however, how variation in overall rates of NCDs impacts the age profile of COVID16
Figure 2: Contract Effects on Default Outcomes Over Time <!-- Start of picture text --> a) Share of Principal In Default<br>.2<br>.18<br>.16<br>.14<br>.12<br>.1<br>.08<br>.06<br>.04<br>.02<br>0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2<br>Proportion of initial loan duration elapsed<br>b) Share Who Miss Scheduled Payment<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2<br>Proportion of initial loan duration elapsed<br>c) Cumulative Share Who Miss a Scheduled Payment<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2<br>Proportion of initial loan duration elapsed<br>d) Share of Principal Repaid<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>.3<br>.2<br>.1<br>0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2<br>Proportion of initial loan duration elapsed<br>Flex Contract Mean Std. Contract Mean<br>95% CI Extension Pass<br>No-Extension Pass<br><!-- End of picture text --> Notes: The graphs use FMSD's monthly administrative data to show treatment effects over the course of the loan. Graphs show the mean in the standard loan group at a given point in time (dashed yellow lines), mean of the standard loan group plus flexible contract treatment effect (solid blue lines) including 95% confidence intervals (dotted blue lines). Regressions are based on monthly data. Since loans in our sample differ in length, we show the share of loan duration elapsed on the horizontal axis rather than months. We use a loan's original length to make flexible loans and standard loans comparable. We round the share of the loan elapsed to the nearest increment of 0.025 with linear interpolation for values in bins between data points for each loan. We use a similar process for the pass-use bar graph. Pass-use bar graphs are based on bins of 0.0833 (1/12) that match the modal 12-month loan length.
of climate change on global tropical cyclone damage. _Nature Climate Change_ , 2(3):205– 209.

- Nordhaus, W. D. (2006). The economics of hurricanes in the United States. Technical report, National Bureau of Economic Research.

- Pita, G., Gunasekera, R., and Ishizawa, O. (2015). Windstorm hazard model for disaster risk assessment in Central America. Working Paper.

- Saleh, F. and Weinstein, M. P. (2016). The role of nature-based infrastructure (NBI) in coastal resiliency planning: A literature review. _Journal of Environmental Management_ , 183:1088–1098.

- Sandilyan, S. and Kathiresan, K. (2015). Mangroves as bioshield: An undisputable fact. _Ocean & Coastal Management_ , 103:94–96.

- Simpson, R. H. and Saffir, H. (1974). The hurricane disaster potential scale. _Weatherwise_ , 27(8):169.

- Spalding, M., Blasco, F., and Field, C. (1997). _World Mangrove Atlas_ . Okinawa Japan: International Society for Mangrove Ecosystems. 178 pp. Compiled by UNEP-WCMC, in collaboration with the International Society for Mangrove Ecosystems (ISME). (version 3). URL: https://archive.org/details/worldmangroveatl97spal.

- Spalding, M., Kainuma, M., and Collins, L. (2010). _World Atlas of Mangroves_ . Earthscan, with International Society for Mangrove Ecosystems, Food and Agriculture Organization of the United Nations, The Nature Conservancy, UNEP World Conservation Monitoring Centre, United Nations Scientific and Cultural Organisation, United Nations University, London.

- Temmerman, S., Meire, P., Bouma, T. J., Herman, P. M., Ysebaert, T., and De Vriend, H. J. (2013). Ecosystem-based coastal defence in the face of global change. _Nature_ , 504(7478):79.

- Vickery, P. J. and Skerlj, P. F. (2005). Hurricane gust factors revisited. _Journal of Structural Engineering_ , 131(5):825–832.

- Zhang, K., Liu, H., Li, Y., Xu, H., Shen, J., Rhome, J., and Smith III, T. J. (2012). The role of mangroves in attenuating storm surges. _Estuarine, Coastal and Shelf Science_ , 102:11–23.

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proxy for non-traded gods, as a share of GDP, for the four remittance receiving countries of CASC.<sup>3</sup>

**Table 7 Total Expenditure and Services as Percent of GDP, Armenia, Georgia, Kyrgyz Republic and Tajikistan: 2000-18**

||2000‐04|2005‐09|2010‐14|2015‐18|
|---|---|---|---|---|
||%|%|%|%|
|Armenia|||||
|Total Expenditure/GDP|119.7|120.7|121.4|112.3|
|Services/GDP|||46.3|50.4|
|Georgia|||||
|Total Expenditure/GDP|115.1|123.5|117.3|114.2|
|Services/GDP|52.3|56.9|59.4|58.3|
|Kyrgyz Republic|||||
|Total Expenditure/GDP|105.0|130.0|141.6|135.6|
|Services/GDP|35.2|45.4|49.7|50.5|
|Tajikistan|||||
|Total Expenditure/GDP|109.2|136.1|145.0|121.5|
|Services/GDP|33.5|43.1|43.2|42.1|

Source: WDI For the two countries which are the largest recipients of remittances as a share of GDP, there was a clear and marked expansion of total expenditure as a share of GDP since the beginning of the century. In both the Kyrgyz Republic and Tajikistan, total expenditure rose by approximately 36 percentage points of GDP from 2000-04 to 2010-15, mirroring the steep rise in remittances to GDP shown in table 4. In Tajikistan, the subsequent fall in remittances was also accompanied by a sharp decline in total expenditure as a share of GDP in 2015-18. There was a much smaller fall in total expenditure in 2015-18 in the Kyrgyz Republic, where remittances were more stable. In both the Kyrgyz Republic and Tajikistan, services expanded their share of GDP, by nearly 15 and 10 percentage points respectively from 2000-04 to 201014, before flattening out. The fall in total expenditure in 2015-18 did not lead to a significant decline in services as a share of GDP in either country.

The trends in Armenia and Georgia are less clear cut. In Armenia total expenditure increased only marginally from 2000-04 to 2010-14 and then fell back quite sharply, by nine percentage

> 3 Total expenditure is the sum of consumption and investment and Final is derived by adding the trade deficit to GDP.

9
- **Mittal, Shekhar and Abhijit Mahajan** , “VAT in Emerging Economies: Does Third Party Verification Matter?,” 2017. Working Paper.

- **Muralidharan, Karthik, Paul Niehaus, and Sandip Sukhtankar** , “Building State Capacity: Evidence from Biometric Smartcards in India,” _American Economic Review_ , October 2016, _106_ (10), 2895–2929.

- **Naritomi, Joana** , “Consumers as tax auditors,” _American Economic Review_ , 2019, _109_ (9), 3031–72.

- **Nicolaides, Panayiotis** , “Threshold Targeting, Misreporting and Adjustment Costs: Evidence from a Third-Party Reporting Policy,” 2021. Working Paper.

- **OECD** , “Tax and Digitalisation,” OECD Going Digital Policy Note, Organisation for Economic Cooperation and Development 2018.

- **Okunogbe, Oyebola and Fabrizio Santoro** , “The Promise and Limitations of Information Technology for Tax Mobilization,” World Bank Policy Research Working Paper, WPS9848 2021.

- **Pomeranz, Dina** , “No Taxation without Information: Deterrence and Self-Enforcement in the Value Added Tax,” _American Economic Review_ , 2015, _105_ (8), 2539–2569.

- **Prais, S. J. and C. B. Winsten** , “Rend Estimators and Serial Correlation,” Working Paper, Cowles Commission 383 1954.

**Rogoff, Kenneth** , _The Curse of Cash_ , Princeton University Press, 2016.

- **Slemrod, Joel B., Brett Collins, Jeffrey L. Hoopes, Daniel H. Reck, and Michael Sebastiani** , “Does Credit-Card Information Reporting Improve Small-Business Tax Compliance?,” _Journal of Public Economics_ , 2017, _149_ , 1–19.

- **United Nations** , “Measuring Tax Transactions Costs in Small and Median Enterprises,” Technical Report, United Nations and Inter-American Centre of Tax Administrations 2014.

- **World Bank Group** , “Electronic Payments Acceptance Incentives Literature Review and Country Examples,” Technical Report, World Bank Group and Financial Inclusion Global Initiative 2014.

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# **1. Introduction**

Paraguay used to be one of the less volatile economies in the Latin America. However, this changed at the turn of the century, when the country’s economic growth became one of the most volatile in the region. Thus, Paraguay’s GDP volatility during 1960-1999 was below average for the Latin American countries (see Table 1). Based on the percentage std dev of GDP growth rate Paraguayan volatility was just 3.88 compared to 4.72 average and 4.59 median volatility in the region. Similar result holds for an alternative measure of volatility – percentage std dev of output gap -- based on which Paraguayan GDP volatility was 4.22, well below the average (4.73) and median (4.98) volatility in the region.<sup>4</sup> This contrasts with the last decade when the volatility in Paraguay has exceeded both, the regional average and median. In fact, during 2000-2011, the volatility of GDP growth rate in Paraguay was the fourth highest in the region after República Bolivariana de Venezuela, Argentina and Trinidad and Tobago. This rise in GDP volatility in Paraguay is particularly striking on the backdrop of falling volatility in the rest of the Latin America during the same period (see Table 1).

Studying volatility in Paraguay, thus, is highly relevant, in particular due to potential negative effects that high volatility can have on growth and equity. First, volatility may lead to lower growth in Paraguay. Hnatkovska and Loayza (2005) show that the link between volatility and growth, to a large extent, is driven by the level of economic development. In particular, they show that in high income countries higher GDP volatility is associated with higher growth, the relationship is weak in middle income countries, but is strongly negative in low-income countries (see Figures 1- 1 below). The negative effect of volatility is also economically significant for this group of countries: each std. dev. increase in volatility is associated with 0.56 percent decline in GDP growth.<sup>5</sup> Second, macroeconomic volatility may have a negative effect on equality.<sup>6</sup> According to Breen and Garcia-Penalosa (2005) a country like Chile could reduce its Gini coefficient by 6 points if it were to reduce its volatility to the same level as Sweden or Norway. Motivated by these observations, in this study we perform a detailed analysis of the sources of GDP volatility in Paraguay during 1994-2012.<sup>7</sup>

> 4 Output gap is measured as the deviation of log GDP from its Hodrick-Prescott (HP) filtered trend. 5 See Table 7 in Hnatkovska and Loayza (2005).

> 6 See for example Breen and Garcia-Penalosa (2005), Garcia-Penalosa and Turnovsky (2003) or Huang, Fang, and Miller (2012).

> 7 The period of study is dictated by the availability of quarterly data for the relevant variables.
**Table 7 (Continued)**

||**Log Total Calorie**|**Consumption**|**Food Co**|**nsumption Score**|
|---|---|---|---|---|
||IHPS|FIAS|IHPS|FIAS|
|**Poorer HH Sample**|||||
|Current Shock|0.040|-0.039|-0.363|2.134|
||(.050)|(.104)|(1.655)|(2.586)|
|Sequential Shock|-0.118|-0.372<br>**|-5.540|**<br>-1.916|
||(.119)|(.159)|(2.716)|(4.112)|
|Aid|-0.104|-0.176|3.305|7.235|
||(.172)|(.217)|(5.309)|(6.288)|
|Aid * Current Shock|-0.128|-0.107|-4.201|-10.257|
||(.223)|(.258)|(5.300)|(6.326)|
|Aid * Sequential Shock|0.139|0.598<br>**|8.878|3.716|
||(.304)|(.304)|(6.506)|(7.304)|
|# of Obs.|2135|1158|2135|1158|
|R2 (within)|0.181|0.197|0.269|0.255|
|R2 (between)|0.348|0.334|0.310|0.368|
|R2 (overall)|0.299|0.288|0.307|0.335|
|**Sept-March Sample**|||||
|Current Shock|-0.046|-0.068|-1.895|3.705|
||(.039)|(.092)|(1.691)|(2.959)|
|Sequential Shock|-0.179<br>*|-0.170|-6.087|*<br>0.069|
||(.099)|(.137)|(3.357)|(4.418)|
|Aid|-0.175|-0.274|2.039|9.354|
||(.177)|(.225)|(7.115)|(8.113)|
|Aid * Current Shock|-0.056|0.271|-6.329|-9.759|
||(.228)|(.266)|(8.020)|(9.095)|
|Aid * Sequential Shock|0.479<br>**|0.475<br>*|14.873|**<br>5.288|
||(.223)|(.259)|(7.265)|(9.179)|
|# of Obs.|1968|1068|1968|1068|
|R2 (within)|0.225|0.224|0.233|0.198|
|R2 (between)|0.420|0.410|0.592|0.546|
|R2 (overall)<br>**Notes:*****/**/* denote statistic<br>**Current Shock**is a dichotomous v|0.359<br>al significance at 1/5/10 pe<br>ariable that is equal to 1 if t|0.327<br>rcent respectively<br>he household has|0.505<br>. Standard E<br>been expos|0.401<br>rrors in parentheses.<br>ed to a weather shock|

**Notes:** ***/**/* denote statistical significance at 1/5/10 percent respectively. Standard Errors in parentheses. **Current Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock _only_ in the current period, and 0 otherwise. **Sequential Shock** is a dichotomous variable that is equal to 1 if the household has been exposed to a weather shock in consecutive periods, and 0 otherwise.

41