rafmacalaba/data-use-mentions-extended huggingface dataset:
the product would be determined by an agronomic model.

As another example, all Kharif maize products sold across India might include four phases of deficit rainfall cover, one phase of consecutive dry day cover, and one phase of excess rainfall cover. All such products would have identical exits, rates, maximum payments, periods, and trigger days. However, triggers for deficit rainfall cover could be uniformly increased or decreased for each maize product so that the calculated rate was within the required range. For example, if the triggers for the four phases of one maize product were {50, 70, 80, 40} then the triggers for any other maize product would be {50+X, 70+X, 80+X, 40+X} for some number of millimeters X, that could be positive or negative. X would be chosen for each product so that the Pure Premium Rate is within the target range. Such an approach would shield the design process from statistically insignificant features of historical weather data. Such dependence would not lead to data mining.

The suggested ratemaking procedure would allow insurers more flexibility in designing products based on agronomic principles. If the difference in rainfall for a group of weather stations in some Risk Collective was not statistically significant, then the same product could be sold in all stations at the same, or a very similar, price. This would have been difficult to justify under the existing ratemaking methodology.

Although the above discussion focuses on WBCIS, the importance of standardizing the shape of products, based on agronomic principles, and using historical data only to determine one or two key parameters is equally valid for any other types of products offered by insurers.

One might think of a weather insurance product as comprising a _shape_ of cover in addition to a _level_ of cover. The shape of cover should not be driven by individual weather data histories because, otherwise, products are likely to be underpriced. It might seem natural for the shape of cover to depend on agronomic fundamentals. In contrast
30.5|-13.0|-1.0|-12.0|
|2011|Jun|26.5|-4.0|-0.4|-3.6|
||Sept|24.5|-2.0|-2.2|0.2|
||Dec|18.5|-6.0|-1.8|-4.2|

Source: Authors’ calculations based on CBE monthly reports. Notes: 1. International reserves + foreign assets of the CBE.

2. Change in (1) 3. Current account from the CBE monthly reports 4. (2) – (3)
A final issue is that of weights. The outcome variables are means of household indicators taken at the regional level, although these are demeaned when predicting the evolution of the shock. When constructing these regional averages, we use household weights. Because the number of observations varies by region, country, and survey weight, it is appropriate to correct for heteroscedasticity. We therefore weight each region-wave observation by the number of households in the original phone survey data used to construct the average for that region.<sup>9</sup> This gives greater weight to regions for which the outcomes are measured with greater precision. These weights are applied both in model selection and in model estimation.

2.4 LASSO Model Selection To construct predictive models, we use LASSO to select which variables among the real-time indicators best predict changes in income, work status, food insecurity, behavioral changes, and educational disruptions. When predicting initial impacts, the dependent variable is the regional average, and when predicting the subsequent evolution of the crisis the dependent variable and the predictors are demeaned using the regional mean over multiple waves. The full set of real-time indicators are included as candidate variables. We use a variant of the LASSO that selects the penalty parameter lambda to minimize the Bayesian Information Criteria, following Zhang et al. (2010). When selecting models to predict in the first wave, both the LASSO and the resulting post-lasso estimates are clustered on country, and when predicting changes over time, the LASSO and the post-lasso estimates are clustered on country-wave.<sup>10</sup> It is not straightforward to apply Lasso to multiply imputed data (Du et al., 2020) and different approaches have been proposed. For the purpose of maintaining simplicity and to avoid overfitting, we select the model using only data from one arbitrarily selected imputation, and report that R<sup>2</sup> . In addition, however, we report the mean R<sup>2</sup> from 100 models
its business, Buenos Aires' situation appears to be distinct from those of other public provincial banks.<sup>13</sup> Therefore, Buenos Aires is treated separately in much of the analysis.

Using balance sheet data from between 1991 and 1996, Appendix 11 estimates the rate at which provincial, private, and privatized provincial banks' net worth declined relative to total liabilities (and assets). The data indicate that loss rates were, on average, much higher for public provincial banks than for privatized or private banks. Based upon this data, we derive counterfactual estimates of the cost of re-capitalizing typical public provincial banks under a variety of scenarios.

In the simulations, the ratio of net worth to total liabilities (NW/TL) of the public provincial bank is assumed to fall at the average rate that NW/TL fell for public provincial banks (except Buenos Aires) between 1991 land 1996 (i.e. 9%). It is also assumed that the province re-capitalizes the simulated bank every three years so that its ratio of net worth to total assets is 11.5%, the level dictated by Argentine prudential regulations.14 Finally, it is assumed that nominal liabilities grow at a rate of 11% per year, the average rate for public provincial banks during the period. For a bank with 25,000,000 pesos in reported net worth, a level very close to the median for public provincial banks (except Buenos Aires) from 1994-96, the nominal re-capitalization payments would total over 205.5 billion pesos if it remained in public hands.<sup>5</sup> 13

- As the flagship of the public banking system, Buenos Aires may be treated somewhat ditferently by regulators than other public provincial banks. A full discussion of how, and why, Buenos. Aires can be publicly owned and yet _apparently_ function more effectively than other state-owned banks is beyond the scope of this paper.

- 4 The qualitative results of the re-capitalization simulations presented here are
within this framework, as Equation (1) is a _bona fide_ structural model in its own right. However, equation (A1.6) allows us to better illustrate the advantages of using the household-level data to correct for a variety of potential econometric problems associated with using the aggregated data, and to precisely highlight the role of aggregation.

# Appendix 2: Expanded Discussion of Panel Data Models

The discussion in this appendix provides further detail on the panel data analysis presented in Table 5 and discussed in Section 4.5. As noted in the body of Section 4.5, the analysis summarized in Table 5 first examines the relationship described in Equation (4) between growth and lagged inequality over short time periods, and then implements first-differenced models that eliminate the effects of fixed village unobservables. It then examines whether controlling for biases potentially introduced if growth and lagged income are both systematically related to a dynamic unobservable in the error term.

The base OLS specification is presented in column (1) and suggests that when short-term growth rates are regressed on lagged inequality, the results are statistically insignificant whether we use the Mean Log Deviation or the 90-10 ratio as our inequality measure. Column (2) shows straight first-differenced (FD) models that sweep out unobserved fixed effects, employing differences of increasing duration from short (one-period) to long (four-period) differences. In these first-differenced models, there is no significant effect of growth on inequality, and if anything, there is a positive (though insignificant) effect as one expects with a Kuznets process.

Dynamic issues arise with the concern that change in growth and lag growth (or lag change in levels) will be may be systematically related through a common error component. Remember that change in growth (the dependent variable) is calculated as (ln _yit_ − ln _yit_ −1)<sup>−</sup> (<sup>ln</sup><sup>_y_</sup> _it_ −1 − ln _yit_ −2 )<sup
# **2. Data and Methodology**

# _2.1 Data_

The paper uses specialized multi-national cross-sectional data from the first round of the 2020 COVID-19 Disparities Survey to identify the multidimensional impacts of the COVID-19 pandemic on cismen, ciswomen, transgender, and non-binary people. The first round of the 2020 COVID-19 Disparities Survey was collected between April–May 2020 using the social networking app Hornet. Hornet is a queer social networking app with over 30 million users worldwide, including in South Asia, East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, and Sub-Saharan Africa (Santos et al. 2020). Hornet users aged 18 years and older were invited to participate in the survey and 7,427 responses were obtained. While the survey may not be representative of the LGBTI population, nonetheless, it collects valuable information from Hornet users on the impact of COVID-19 on their economic condition, mental distress, and HIV prevention, testing, treatment, and care.

For this study, we categorize gender identity based on the survey question asking respondents to define their identity. We group respondents as cismen, ciswomen, transgender, and non-binary people based on their responses.<sup>2</sup> Note that the gender identity response categories of cismen, ciswomen, transgender, and non-binary people capture respondents of varying sexual orientations such as heterosexual, gay, lesbian, bisexual, and others.<sup>3</sup> The sample in this study consists of the cismen, ciswomen, transgender, and non-binary people for whom all the information on the selected income, consumption, mental distress indicators, and individual, household, and regional characteristics are available. Table 1 summarizes the sample distribution of these individuals by their gender identity. It is important to note that the sample

> 2 As respondents were allowed multiple responses to identify their gender identity, we consider individuals with more than one response non-binary.

> 3 In the survey, respondents
Converting the intercepts on either side of the threshold from logs into levels implies that binding vessels just above their nearest gross tonnage threshold receive about $10,000 more in fuel subsidy per month than binding vessels just below their nearest gross tonnage threshold (approximately $24,000 per month compared to $14,000 per month). The elasticity of fishing hours with respect to fuel subsidy received is 2.177 (Column 3). While the conventional first stage and elasticity estimates are large in magnitude, neither are statistically different from zero when we apply our randomization inference procedure. The randomization inference p-values are 0.187 and 0.101 (Figure A5), compared to conventional p-values of 4 _×_ 10<sup>_−_7</sup> and 0.0004. We therefore view these estimates as suggestive evidence that the elasticity of fishing with respect to fuel subsidies may be large, perhaps even larger than 1 (conventional p-value = 0.029). Our estimates could be imprecise because we estimate fuel subsidies with different transponder data than the regulator uses in assigning subsidies (Section 2).<sup>22</sup> We use a similar procedure to estimate the elasticity of distance traveled with respect to fuel subsidy received. Columns 4 to 6 of Table 4 display the reduced form, first stage, and elasticity estimates. As in Column 3 of Table 3, we apply the optimal bandwidth from when log fishing hours was the dependent variable (to ensure comparability). There are more observations than when we estimate the fishing hours elasticity because of vesselmonths with positive distance traveled and subsidy received but with zero fishing hours. The two first stage estimates differ for this reason.<sup>23</sup> The first stage is more precise in this specification (conventional p-value is 5 _×_ 10<sup>_−_7</sup> and randomization inference p-value is 0.057). We find that the elasticity of distance traveled with respect to
<!-- Start of picture text --> Stunting prevalance (%)<br>0-5 completed months 6-18 completed months 19-60 completed months<br>All individuals- g 2 2<br>Income quintile- | 75, gk - g &<br>Mother's education- oR g & 3 g<br>Urban/rural HH residence- 11 %3 oe %, gS 2<br>Sex" | jig % 38738<br>0% 20 40 55% 0% 20 40 55% 0% 20 40 55%<br><!-- End of picture text -->

© Poorest 20% © Some or complete college © Urban © Girls © Richest 20% © Some or complete elementary; or no schooling © Rural © Boys Source: Author's calculations using 2015 NNS survey data. Notes: 95% confidence intervals of the stunting prevalance by age group and population subgroup are displayed.
of people with different incomes and educational attainment levels. First, we use data from the World Bank’s Living Standards Measurement Surveys to estimate durables Engel curves. We provide new evidence on the extensive margin of demand for a variety of carbon intensive durables – cars, motorcycles, air conditioners, refrigerators, computers, televisions, washing machines and cell phones. If a nation’s electricity grid could be cheaply decarbonized then such consumers would not bear the incidence of a carbon tax. Owners of internal combustion engine motorcycles and vehicles would face the carbon tax burden as their asset’s resale value would decline and its operating cost would increase.

3
Charging Station|||||36.34|
|Underidentifcation Test||||62.60|77.22|
|Weak Identifcation Test||||31.85|22.47|
|Overidentifcation Test||||55.16|62.93|
|Observations|4528|4528|4528|4528|4528|

_Notes:_ The regressions are based on data for 13 countries from 2013 to 2020. Observations for China in 2020 are excluded due to the impact of COVID19. The dependent variable is the logit share in logit demand model. _Price - Incentive_ variable is constructed from tax inclusive price subtracting total incentive received. Column (4) shows 2SLS estimates using instruments for consumer prices using the battery supplier dummy interacted with battery capacity as well as BLP instruments including the number of models and models’ size, battery capacity, and range for both own brand and rival brands. Column (5) in addition instruments for the number of charging ports using heavy duty EV stock, construction labor costs, and their interactions. Standard errors are clustered at the country by year level and reported in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 S6
# **I. Introduction**

The substantial positive returns to education for both individuals (e.g. Psacharopoulos and Patrinos, 2004) and countries (e.g. Barro and Lee, 2010) are a fairly established fact in the literature of the economics of education. It comes as no surprise, then, that policy makers and academicians are so interested in understanding the factors that can influence the quantity and quality of education.

While the academic literature on these factors is vast, most of it focuses on developed countries, and while there is some literature on developing countries, only recently have studies using data from countries in Eastern Europe and the Former Soviet Union (henceforth, _transition countries_ ) started to appear. The recent surge in this literature can be explained by the fact that performance measures for these countries have become available, both through the participation of these countries in international assessments of student learning like the Organisation for Economic Cooperation and Development (OECD)‟s Programme for International Assessment (PISA), the Trends in International Mathematics and Science Survey (TIMSS) and Progress in International Reading Literacy Survey (PIRLS), and through the introduction of centralized nation-wide assessments in several transition countries.

In terms of the development of their educational systems, transition countries may be considered to lie somewhere between developed and developing ones. Transition countries typically have had mandatory basic education and enjoy very high levels of school attendance. As a result, so the issue is not how to get children to attend school, as it is in a big part of the developing world. Instead, transition countries face the challenge of how to modernize and reform their educational system so as to provide quality education at an affordable price.

One possible opportunity to realize such quality enhancing reforms might come from taking advantage of the so-called “demographic dividend”, i.e. the sizeable demographic changes that took place in most of the transition countries, especially in the last 20 years. In fact, after the fall of the Soviet Union
text --> _Source:_ Authors’ calculations. DDP Data, World Bank.

**However, Togo has not always exhibited a grim social and economic performance.** In fact, almost half a century ago, Togo began its post‐independence economic journey with a fast‐paced sustained growth, which permitted its per‐capita income to almost double in a span of two decades. An economic collapse after 1980 had wiped out most of the post‐independence improvements in living standards by the early 1990s. If Togo had maintained its average growth rate from the 1960s (5.8% per year per capita), it would have achieved middle‐low‐income status today, with a current US$ per capita GDP comparable to that of Philippines, Honduras and Egypt in 2008.<sup>3</sup> Instead of catching up, per capita income in Togo has diverged from the average of peer low‐income countries, Sub‐ Saharan Africa and the rest of the world (See Figure 1 and Table A1, Appendix).

> 1 Among 192 countries with data reported for 2008, and out of 233 registered by World Bank. Cross country comparisons of income levels, regarded as proxies of individual welfare, are more accurate when presented in Purchasing Power Parity terms (PPP) than in current or real US$. In simple terms, PPP is the ratio of prices for the same quantity of a particular commodity in two countries, when the prices are expressed in each country’s currency. In 2007, the PPP conversion factor was 224.6 XOF (Communaute Financiere Africaine Francs) per US$, implying that, to buy 1 US$ worth of the PPP basket in Togo, one would have needed 224.6 XOF. In turn, the official exchange rate in Togo was 479.3 XOF/US$. At this rate one would have purchased more of the selected PPP basket in Togo  using XOF, than the amount of that basket purchased
# **4 Testing the EFC hypothesis with VAR structural shocks**

In this section, I test the EFC hypothesis empirically by using structural shocks estimated from an identified VAR as an indirect measure of fiscal austerity. Proxy structural VAR, a relatively new empirical methodology in this literature, is employed to estimate the structural shocks. I discuss data and the PVAR identifiation scheme before presenting main empirical results with two alternative definitions of high-debt regime.

## **4.1 data**

My sample consists of annual data for 17 OECD countries between 1978 and 2009<sup>1</sup> . These countries are: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Netherlands, Portugal, Spain, Sweden, United Kingdom and United States. Quarterly data would have increased the precision of my estimates and allowed for the estimation of a separate structural VAR for each country, but spending and tax revenue data are not available at quarterly frequency for a large fraction of the country-years I considered.

_Yt_ and _Tt_ are _Gross Domestic Product_ and _Tax Revenue_ from OECD statistics. _Gt_ is _Government Expenditure_ from the recently constructed IMF Historical Public Finance Dataset described in Mauro et al.(2015), the most comprehensive source of fiscal flows and stocks to date, where the debt-to-GDP ratios are also taken from. All three variables in the SVAR are in logs, per capita, real and in dollars. The narrative fiscal series are from Devries et al(2014), where the authors document the exact sources, methodology and construction of these discretionary changes in taxes and spending, drawing extensively on policy documents and reports to identify the motivation of such changes. Guajardo, Leigh and Pescatori(2014) use this dataset to study expansionary fiscal contractions.

> 1Final regressions are for the years 1981-2009, since I estimated the VARs with two lags and variables are

16
or the partnering tenant, shows that tenants‟ productivity is higher than that of landlords by between 17 and 26 percentage points suggesting that land rental could significantly enhance productivity. Still, this is counteracted by contractual arrangements; in fact we cannot reject the hypothesis that, despite tenants‟ productivity advantage, productivity on leased plots is no different from those self-cultivated by the landlord. As this result is unlikely to be driven by unobserved land quality, use of sharecropping rather than fixed rental arrangements implies that rental markets fail to realize their productive potential. Predominantly female landlords will draw little, if any benefits from having their land sharecropped as compared to cultivating it themselves. Policies such as insurance that would allow adoption of fixed rent contracts could thus increase welfare by both tenants and landlords.

The paper is structured as follows. Section two introduces the topic and uses descriptive statistics from household- and plot-level data to illustrate the setting and highlight evidence on working of rental markets from data on transaction partners. Section three discusses literature on the efficiency of sharecropping and introduces our analytical framework that extends the standard approach to allow for arrangements that might allow partners to reduce inefficiency of certain contractual arrangements and to assess potential and actual productivity differential between tenants and landlords. Section four presents results from the econometric analysis. Section five concludes by discussing potential policy implications.

# **2. Background and descriptive statistics**

Our data come from a rural environment with limited non-agricultural labor markets and high levels of land rental market activity. High risk, social norms regarding females‟ roles, and large differences in wealth and endowments of critical factors, in particular oxen, imply that land is mainly transferred from poor and often female headed landlords to relatively more affluent tenants with draught power. Contrary to what is observed elsewhere, rentals thus lead to greater concentration of operational holdings. Virtually all rentals involve sharecropping rather than fixed rental contracts. Descriptive results suggest that intensity of input use is lower on sharecropped than on owned plots.

# **2.1 Household characteristics*