rafmacalaba/datause-encoder-data huggingface dataset:
This move to an actuarial regime is made possible by risk-based pricing whereby the commercial premium rate for each product, accounting for the full commercial cost of providing cover, is paid to the insurer by farmers and government at the start of the crop season (Clarke et al. 2011a). The premium rate paid by farmers is increasing in the commercial premium rate, improving agronomic signaling as compared to the flat premium rates of NAIS (Figure 5). The product design formula for mNAIS also differs from that for NAIS, with the intention of reducing the local variation in commercial premium rates for each crop; the previous probable yield rule, equation ( 3), would have led to a large variation in the value of benefits, and therefore a large variation in commercial premium rates. Improved risk classification is also expected to reduce adverse selection and increase equity amongst farmers by spreading government subsidy more evenly.

Figure 5: Split of mNAIS commercial premium rate <!-- Start of picture text --> 20%<br>15%<br>10% Premium rate paid<br>by government<br>6%<br>5%<br>3%<br>2% Premium rate paid by farmers<br>0%<br>0% 5% 10% 15% 20%<br>Commercial Premium Rate<br>Premium Rate<br><!-- End of picture text --> Source: AICI (2010)

# **5. Remaining challenges and suggestions for future research**

The move to mNAIS, whilst a very positive step forward, introduces new challenges.

**Improving robustness of yield estimation:** The implementation of mNAIS at a lower insurance unit level (village Panchayat), while offering the prospect of lower basis risk for farmers and therefore providing improved benefits, creates both challenges and costs for the state governments in the implementation of the crop cutting experiments (CCEs).  These need to be addressed in the medium term. Moving to a lower insurance unit for major crops significantly increases the number of CCEs to be conducted, as at least 8 CCEs have to be conducted for each of these crop in each Gram Panchayat. While mNAIS is currently being piloted in only 50 districts, issues related to the costs, potential outsourcing of CCEs, quality control including possible applications of technology, and sampling framework need to be addressed in advance of wider expansion.

To deal with the challenge of significantly increased number of CCEs, new technology and partial outsourcing of the yield estimation process could be piloted to increase the efficiency of the CCE process and to reduce the number of CCEs. Technology could be utilized in the conduct of official CCEs, for example by requiring that all CCEs are video recorded on inexpensive mobile phones. Insurers and state governments could watch a random selection of CCE videos, to verify that the correct procedure was being followed and to highlight areas for future training of field staff. CCE data could be transmitted to the insurer on the day each CCE is conducted to allow real time monitoring by local staff and comparison with other data sources, such as remote sensing imagery. Remote sensing imagery could also be used to reduce the number of CCEs needed: a higher number 16
25 The story in Peru is somewhat different, because outright populist policies played a big role in starting.the hyperinflation. In this respect, the causes were clearer. Nevertheless, after the initial outbreak, the Peruvian hyperinflation and the ensuing stabilization process shared many common elements with those of Argentina and Brazil. In this respect, it does not look like a CH.

It is useful to take another quick look at the relationship between seigniorage and inflation in these three countries (this time using monthly data) before examining each of these experiences more closely. Figure 3 shows the monthly inflation and estimates of the revenue from money creation (or seigniorage) for the three countries.7 It is clear that in Peru, the beginning of the hyperinflation era was linked to excessive money creation in 1985 and 1986, which eventually led to an explosion in inflation starting in 1988. Lxkewise, the hyperinflation outburst of 1990 was oreceded by a large increase in seigniorage. This episode resembled the CH. In contrast, the relationship between seigniorage and inflation is less clear in Argentina and Brazil. In both cases, seigniorage appears to have increased in response to the beginning of the hyperinflation rather than the opposite as a result of an extreme Olivera-Tanzi effect. Inflation wa's pulling up seigniorage in the hyperinflations of 1989.

This evidence indicates that thg causes of the recent episodes are not as clear as in the classical cases. In what follows we will examine each experience in more detail and indicate in which respects the new episodes are different.

The revenue from money creation is calculated as the change in the money base divided by the price level.
18 national level but constant at the urban level. The road length results are the reverse. And the vehicle-road ratios show evidence of variability at both the national and urban levels.

The second question addressed regarding equation specifications involved comparing cross section (long run) elasticities with time series or first difference (short run) elasticities. The estimated elasticities resulting from the two different specifications are summarized in Table 6. There are only modest differences between the time series and cross section income elasticities at the national level, but beyond that many differences emerge. The most striking differences between the time series and cross section elasticities emerge among the income elasticities for road length and the vehicle-road ratio at the urban level. These results offer stark evidence that road provision is lagging in urban areas while the size of urban vehicle fleets continues to grow. The consequence is that the urban vehicle-road ratios are growing nearly as rapidly as income. Because vehicle use does not grow as rapidly as vehicle ownership, congestion may not be growing as fast as the number of vehicles, but it is clearly increasing with income.

**Table 6. Comparison of Time Series (T.S.) and Cross Section (C.S) elasticities**

|||Nation|al||||Urb|an|||
|---|---|---|---|---|---|---|---|---|---|---|
|Dependent|Inc|ome|Popu|lation|Inco|me|Popu|lation|Pop.|Density|
|variable|T.S.|C.S|T.S.|C.S|T.S|C.S.|T.S.|C.S|T.S.|<br>C.S|
|Vehicles|1.0|0.9|_0.5_|1.0|1.0|0.8|1.0|0.9|0.1|<br>-0.4|
|Road length|_0.5_|0.5|0.4|1.0|0.1|0.7|0.5|0.8|-0.4|<br>-1.0|
|Vehicle-road ratio|0.4|0.4|0.3|0.0|0.9|0.0|0.5|0.1|0.5|<br>0.6|

**Urban spatial endowments.** The strong relation between income and population density at the urban level had produced unstable results in earlier regressions. Several of the urban equations reported on here use the deviation in area per capita (the inverse of density) from a predicted level for each city to summarize the effect of the durability of urban development patterns. This variable, termed an urban historical endowment index, worked well in the equations.

**Implications.** Many implications flow from the empirical results presented here. Most notably is the increase in urban congestion over time reflected in the remarkably rapid increase in the urban vehicle-road ratio with income over time. The magnitude of these trends is so large that it is alarming. Economic growth appears to be producing urban gridlock and promoting low density urban development.

During recent decades, urban areas have coped with congestion by spreading their activities over larger areas and adding road space by annexation. Decentralized urban development is most evident in high income countries, such as the U.S. and Australia, where land costs at the periphery of urban areas are relatively low. Yet over a 20 year period in the global sample of 35 cities analyzed here, average population densities declined in 25 and the urban area increased in 30, so urban area expansion with
The impact on the poverty headcount only conveys part of the story of the impact of these price rises. A recent paper shows that 88% of the increase in urban poverty depth due to the global increase in food prices is from poor households becoming poorer and only 12% from households falling into poverty (Dessus et al., 2008). This is consistent with evidence from the Indonesian financial crisis in the late 1990s where the impact on poverty depth was higher than on the headcount (Skoufias et al., 2000), as well as with the evidence provided for West and Central Africa by Wodon et al. (2008). Hence, policy responses ought to focus less on identifying the ‘new poor’ and more on scaling up anti-poverty interventions for the existing poor. Moreover the strains caused by higher food prices may lead to distress sales of assets which will aggravate chronic poverty. There is evidence that, over the medium run, households adjust their production and consumption in the face of higher prices (Deaton 1997, Porto 2005). In the short run, households smooth their consumption by increasing their labor supply and drawing down their savings. However, when families have to disinvest in their livelihoods—eating their seed grain, selling their animals — they will be challenged to rebuild their earning capacities and increase the risk of chronic poverty. Inadequate credit markets can exacerbate these constraints<sup>3</sup> as the poor are often forced to borrow from moneylenders at high rates of interest.

The costs to human development outcomes particularly for poor children may be irreversible. As households face shocks to their real income, they eat less and switch from more expensive sources of protein such as fish, meat, and eggs to cheaper coarse cereals. This switch will cause micro-nutrient deficiencies (in iron, iodine and essential vitamins). The poor, moreover, will be forced to cut back on calorie intake, leading to weight loss and acute malnutrition. Evidence from economic crises in the past has shown that the most susceptible are children under 24 months of age, pregnant and lactating women, and those already suffering from malnourishment (Alderman et al 2006, Pongou et al 2005). The adverse impact of shocks on schooling is also well documented (Escobal et al 2005, Duryea 2006). Evidence from a recent survey in Bangladesh suggests that about half the households surveyed reduced spending on education to cope with rising food prices. Policy responses to minimize the potential effects of higher food and fuel prices on schooling are thus essential as even a temporary gap in attendance can impose serious costs in a child’s educational attainment.

> 3 Adequate access to credit can help families avoid negative coping strategies, but the poor often lack access to credit or have access only on particularly onerous terms (for example, from moneylenders). Where credit is available, it can lead to large indebtedness, which can have repercussions on family welfare for years to come.
Renkow, M., 2006, “Cities, towns and the rural nonfarm economy, in Haggblade, S., P. Hazell and T. Reardon (eds), _Transforming the Rural Nonfarm Economy_ , Baltimore: Johns Hopkins University Press.

Rodriguez-Clare, Andres, 1996.The division of labor and economic development, Journal of Development Economics, Elsevier, vol. 49(1), pages 3-32, April.

Sarker, M. A. R., Alam, K., and Gow, J. (2012). Exploring the relationship between climate change and rice yield in Bangladesh: An analysis of time series data. _Agricultural System_ , pp. 11-16.

# **Online Appendix: Not for Publication**

PROOF OF PROPOSITION 1 (1.1) The labor market equilibrium in equation (8) in the text can simplified as: <u>1</u> where ∆4 = _υT_ ∆3 + _υs_ and ∆5 = _A_ (1 _− α_ ) _α_ (1 + ∆4∆1) _._ The effect of agricultural productivity shock on wage cane be derived from equation (13) as: (1.2) We show that the stated restrictions on land/labor ratio and _Ewθ_ are sufficient for _∂θ∂A_<sup>_∂_2</sup><sup>_w>_0</sup><sup>_._</sup> Using equation (14), we get: where _D_ is the denominator in the RHS of equation (14), and _EwA_ is the elasticity of wage with respect 32
defined as taking more than 200 days according to the _Doing Business_ indicators which record mandated _de jure_ policy implantation times<sup>5</sup> :

## t= βBBribe+ βXX+ βRBurdensome  Regulation+ βBRBribe∗Burdensome Regulation+ e

If corruption is particularly efficient grease when rules are more burdensome one would expect βBR to be negative.

An alternative means to examine whether OLS regressions, which provide estimates of average effects, obscure heterogeneity, whereby some firms (perhaps those that do not pay) suffer from corruption, but others (perhaps those who do pay) benefit, we run quantile regressions, using as dependent variable policy implementation times demeaned by the country-sector-year average, such that the resulting estimates are not driven by cross-country differences. If firms that get things done rapidly do so because they pay bribes, one would expect the coefficient associated with bribe demands to be negative at lower parts of the distribution.

# **3 Data**

The World Bank Enterprise Surveys are the main data source used in this paper. The surveys have been conducted in 107 countries and contain detailed information on firm characteristics, such as their sector, location, sales, capital stock, size, ownership structure, management, and their manager’s subjective perceptions of the business climate and obstacles to doing business.<sup>6</sup> Figure 1 shows how entrepreneurs rated the severity of various constraints, and demonstrates that corruption

> 5 Note that we choose an arbitrary cutoff of 200 days since it is a round number close to the sample median, with 47.8% of firms in our sample being located in countries where it should take less than 200 days to obtain a construction permit. 6 We exclude countries where information on bribes was not collected.

13
de Beer, P., J-L. Rocca (1997), _La Chine à la fin de l’ère DENG Xiaoping_ (Paris, Le Monde-Editions).

- de Braud, A., J. Giles (2008), “Migrant Labor Markets and the Welfare of Rural Households in the Developing World: Evidence from China”, Seminar at the Social Protection and Labor Sector of the Human Development Network of the World Bank.

- de Janvry, A., E. Sadoulet (2001), “Income Strategies Among Rural Households in Mexico: The Role of Off-farm Activities”, _World Development_ , 29(3), 467-480.

- de Janvry, A., E. Sadoulet, N. Zhu (2005), “The Role of Non-Farm Incomes in Reducing Rural Poverty and Inequality in China”, CUDARE Working Papers 1001, University of California, Berkeley, http://repositories.cdlib.org/are_ucb/1001.

- Docquier, F., H. Rapoport (2003), “Remittances and Inequality: A Dynamic Migration Model”, IZA Discussion Paper No. 808, Institute for the Study of Labor, Bonn.

- Elbers, C., P. Lanjouw (2001), “Intersectoral Transfer, Growth, and Inequality in Rural Ecuador”, _World Development_ , 29(3), 481-496.

- Escobal, J. (2001), “The Determinants of Nonfarm Income Diversification in Rural Peru”, _World Development_ , 29(3), 497-508.

FAO (1998), _The state of food and agriculture 1998_ (Rome, FAO).

- Foster, J., J. Greer, E. Thorbecke (1984), “A Class of Decomposable Poverty Measures”, _Econometrica_ , 52(3), 761-766.

- Guzmán, J.C., A.R.Morrison, M. Sjöblom (2008), “The Impact of Remittances and Gender on Household Expenditure Patterns: Evidence from Ghana”, in A.R. Morrison, M. Schiff, M. Sjöblom (eds), _The International Migration of Women_ (Washington D. C., The World Bank).

Heckman, J. (1979), “Sample selection bias as a specification error”, _Econometrica_ , 47(1), 153-161.

- Hussain, A., P. Lanjouw, N. Stern (1994), “Income Inequalities in China: Evidence from Household Survey Data”, _World Development_ , 22(12), 1947-1957.

- Islam, N. (1997), “The non-farm sector and rural development – review of issues and evidence”, Food, Agriculture and the Environment Discussion Paper 22. IFPRI, Washington D.C.

- Jones, R.C. (1998), “Remittances and inequality: A question of migration stage and geographic scale”, _Economic Geography_ , 74 (1), 8-25.

- Kanbur, R., X. Zhang (1999), “Which regional inequality? The evolution of rural-urban and inland-coastal inequality in China, 1983-1995”, _Journal of Comparative Economic_ , 27(4), 686-701

- Khan, A.R., C. Riskin (2001), _Inequality and poverty in China in the age of globalization_ (New York, Oxford University Press).

- Kimhi, A. (1994), “Quasi Maximum Likelihood Estimation of Multivariate Probit Models: Farm Couples’ Labor Participation”, _American Journal of Agricultural Economics_ , 76(4), 828-835.

27
ticularly at the World Bank’s international extreme poverty line of US$1.90. These results are important as they show the potential of cash-transfer programs being effective policy response in times of aggregate adverse events such as the Covid-19 pandemic, natural disasters, or economic crises.

There appears to be a broad agreement that cash-transfer programs can offer effective protection against household-specific negative income shocks. But there has been little systematic research into whether or not this translates into effective protection in the case of large-scale events. There are several reason why this may not be the case, most of which can be thought of as general equilibrium effects. For example, if an entire neighborhood is destroyed and a critical mass of residents decides to leave, a mass exodus may ensue as economic life breaks down and/or public services may be scaled back or cut off completely. Instead of rebuilding their homes with the help of cash transfers, households would lose all their assets. Another example considers the effect on prices: If products of primary necessity become scarce, cash-transfers may further increase their prices while only having a small or negligible effect on consumption levels. This, in turn, may force households to engage in exactly the short-term responses thought to be the root-cause of many poverty traps, such as taking children out of school or liquidating productive assets.

Typhoon Yolanda made land fall in Eastern Samar in the early morning of November 8, 2013. Over the course of the day, it moved westward, making landfall five more times on different islands of the archipelago. Ac3
# **4 Results**

## **4.1 Descriptive Results**

## **4.1.1 Baseline Balance**

Table 1 provides summary statistics of covariate balance at baseline by treatment arm. The respondents in my sample are on average 42 years old. They have 1.9 children on average; 63 percent are married. About one-third of the sample lives in a rural area. Small imbalances are observed with respect to educational attainment. One in four respondents has completed higher education; the percentage is 29 percent in T1 and 23 percent in T2 and control. I control for educational attainment in all regressions and later introduce survey weights as a robustness check.

On average, 42 percent of my sample work full time and 21 percent are homemakers. The Online Appendix compares my sample’s geographical spread by governorate to the latest national statistics (Institut National de la Statistique; 2022a) and shows that it is highly geographically representative. It also compares the share of age cohorts (Institut National de la Statistique; 2022b): younger cohorts are overrepresented in my sample. In the robustness checks, I show that weighing down younger cohorts does not affect my conclusions.

## **4.1.2 Second-Order Beliefs**

The belief that a majority of one’s peers opposes political change may affect individual opinion and behavior. For example, an individual may refrain from taking a particular action or expressing a political opinion because she thinks that society will disapprove of or sanction it. Generalized misperceptions about public opinion may therefore hinder social change and keep society captured in inefficient social equilibria (see Kuran, 1991). When studying socially contentious issues such as the reform of law of religious origin, paying attention to individuals’ perceptions of what is the public opinion - second-order beliefs - is thus crucial.<sup>12</sup> Figures 3, 4, 5 and 6 aggregate respondents’ second-order beliefs about their

> 12I use the term “first-order belief” to refer to an individual’s own beliefs. i.e. “I am favorable to introducing gender equal inheritance law.” I use the term “second-order belief” to refer to an individual’s perception of what others believe. i.e. “I think that the majority of Tunisians are opposed to introducing gender equal inheritance law.” The terms “second-order belief” and perception are thus used interchangeably.

21
# **Appendix B.  Numerical implementation**

153. In this section we present the numerical implementation of the portfolio selection model presented in the previous section. The implementation consists of three stages:

- characterization of the dynamics of the variables of interest (asset returns, contributions to an individual account, minimum wage, etc.)

- estimation/calibration of the parameters involved

- calculating the optimal portfolio for the expected trajectory

## **a.** **<u>The dynamics of quantities of interest</u>**

154. The following specific dynamics were considered from the base of available assets:<sup>49</sup> where is the price of a bond index with duration _,_ , , for with as the _constant_ risk reward associated to the risk factor . In this case, corresponds to the interest rate risk factor, while _{ }_ represents independent risk factors linked to the remaining available assets. For their part, , for . The correlation between assets and , with , coming from .

155. Note that the dynamics considered imply risk reward, volatilities and constant correlations over time, a modeling alternative that is mainly due to the short data series available, which make non-linearity models less reliable in asset dynamics, like mean reversion, or changes of regimen.

## **b.** **<u>Estimation and calibration of parameters</u>**

156. In the calibration process diverse market indices were considered in order to allow a reduction in the dimensionality of the problem and greater stability in the solution.<sup>50</sup> At the domestic level, the asset indices COLCAP and IGBC were considered, as well as the fixed income index IDXTES drawn up by Reveiz and León (2008). Additionally, state bond indices were constructed from the interest rate curves

> **49** The dynamics in question correspond with the Cholesky decomposition of an asset base with correlated shocks, and on that used  by Detemple et al. (2003, §VI.A) and Munk and Sørensen (2010).

> **50** This obeys the variance-covariance matrix of returns which tends not to be positively defined when a relatively high number of assets and their returns are highly correlated.

43