rafmacalaba/data-use-mentions-extended huggingface dataset:
1 should be at least as strong as that for good 2: accordingly, let _a_ = 0 _._ 5. Without loss of generality, set the prices of both goods in the town at unity in all periods. A survey of 30 villages in upland Orissa for the year 2009-10 (Bell and van Dillen, 2012) yields the finding that in the absence of an all-weather road, the unit transport costs for paddy, the main crop, were a bit less than 0.1, but those for other commercial crops somewhat higher. Applying the same to fertilizers, seeds and other goods bought in, households in such villages then face the price vector **p** _t_ = (0 _._ 9 _,_ 1 _._ 1) _∀ t_ . As for the trip to school, the great majority of India’s villages have a primary one of their own; but only a small minority have a secondary or high school, and in the absence of an allweather road, the daily round-trip time can be rather long. The averages for primary and secondary school pupils in the Orissa villages lacking such a road were 23 and 76 minutes, respectively. Allowing, say, twelve hours for sleeping, eating and bathing at home, and taking into account the higher opportunity costs of older children’s time, let _τ_ = 0 _._ 08. The direct costs of state schooling are surely modest: recalling (8), let _σt_ be 0.15 times the opportunity cost factor _p_ 1 _tαt_ (1 + _τ_ ) _γ_ , whereby _αt_ has yet to be determined. For the moment, we also defer discussion of the inter-temporal taste parameters _b_ 1 and _b_ 2.

Coming by estimates of premature adult mortality is a far easier task than that of morbidity. In a setting of three overlapping generations, each generation corresponds to about 20 years, the age at which full adulthood is attained. The rate _qt_ therefore corresponds to 20 _q_ 20,
In the case of the HIES 2016/17, ���<sup>�</sup> � is the 10 percent RSE desired for the mean total household expenditure estimated at the district level; _DEFF_ is the average design effect of the target variable across all districts; ��������� is the coefficient of variation of total household expenditure estimated at the national level; and ��/� is set at 1.96, which corresponds to a – significance level of 5 percent.<sup>15</sup> We use a national estimate of the CV necessary to estimate – the sample size needed to achieve the desired accuracy because we cannot always estimate district level CVs with enough precision based on the HIES 2010 data. Therefore, the above calculations assume that all districts have the same DEFFs and CVs for the target variable of interest. It is possible, that the final HIES 2016/17 sample might estimate district level welfare estimates with RSEs slightly larger than 10 percent, depending on the how close the CVs and DEFFs of the target district variables are to their actual values.

It is also important to note that we focus on district-level subsamples to estimate sample size, because, as mentioned earlier, we expect that achieving accuracy statistics at the district level will be more difficult than at the higher division and quarterly levels of aggregation. By substituting all values in equation (1), we find that the sample size needed is 715 households for each district in order to obtain RSEs of mean total household expenditure below 10 percent. This brings the preliminary total sample size to 45,760 households (715*64 districts) for the HIES 2016/17. However, we allocate 720 households to each district to fit pre-established field protocols used by BBS in the HIES 2010, and to facilitate field work and survey implementation management. More specifically, 720 households is divisible by 36 (i.e. the number of PSUs ultimately allocated to each district), which
redefine the high disruption dummy to be district specific, so that villages in each district that lie in the highest quartile of the continuous disruption index are classified as high disruption. This is the final row of results shown for our main outcomes in Table A6: of the 12 significant coefficients in the baseline specifications, 9 remain statistically significant at conventional levels once we consider high disruption to be within-district rather than in absolute terms across the evaluation sample (with some point estimates being larger in absolute value than in the baseline specification).

As a final check, we reconstruct _Evd_ by additionally using information on the disruption of daily and periodic markets. This again uses data from the village leader survey, where we consider daily markets as disrupted or closed if reported as such by village leaders for at least one month between July 2014 and September 2015. However, a significant number of villages ( _n_ = 62) do not have a daily market. Hence we make the extreme assumption that both dummies for disruption and closure are equal to one in those cases. This gives the most conservative measure of disruptions and, if the assumption is incorrect, will bias estimated impacts of disruptions towards zero. Table A7 then shows statistics related to the components of this modified index: the two additional components related to markets shown in Panel C have considerable within district 39
16% worked in formal public, 14% in formal private and 66% in informal casual sectors. The corresponding numbers for 1999-00 are 14%, 16%, and 66%. In both rounds, the “don’t know” response about the sector of employment among workers in the formal sector averages between 2 and 4% of the sample.

Characteristics of the workforce in these sectors varied substantially. In 1999-00, slightly less than 30% of the total selected sample resided in urban areas, while more than 60% of all public workers and about 70% of all formal private workers did so. Males represented about 85% in the public and formal private sectors and 70% in the salaried

> 6 Changes in the definitions of the public/private categories between the 50th and 55th rounds of the EUS complicate the comparability of 1993 and 1999 employment data. We examined whether the implemented imputations resulted in biasing for or against our main conclusions (i.e. whether the estimated wage differential was smaller or larger with imputation than it was without). It turns out that the public/private differential is lower with imputation, indicating that imputation biases against our results. The results for a sub-sample that excludes individuals with unidentified enterprise type are available from the authors upon request.

11
factor-market explanations, and pointing by implication to an institutional class of explanations. Our excellent cross-village data, with all the benefits of clean and comparable measurement of inequality and growth over time, however, also show the limits of how much can be ultimately learned in a growthinequality regression. Unless a researcher is willing to believe that both the underlying relationship between growth and inequality is stable, and all variation in inequality is driven by the same “treatment,” then little that is definitive can be learned from this exercise. While it is difficult to believe that either condition holds in the Chinese context, it is even harder to believe that such conditions hold in a crosscountry setting.

# 6.0 References

- Acemoglu, Daron. 1996. “A Microfoundation for Social Increasing Returns in Human Capital Accumulation.” _Quarterly Journal of Economics_ , 111(3): 779-804.

- Acemoglu, Daron, Maria Angelica Bautista, Pablo Querubin, and James Robinson. 2008. “Economic and Political Inequality in Development: The Case of Cundinamarca, Colombia,” In: Elhanan Helpman (Ed.), _Institutions and Economic Performance_ . Harvard University Press, Cambridge Massachusetts.

- Aghion, Philippe, Eve Caroli, and Cecilia Garcia-Penalosaand. 1999. “Inequality and Economic Growth: The Perspective of the New Growth Theories.” _Journal of Economic Literature_ , 37(4): 1615-60.

- Alesina, Alberto  and Eliana La Ferrara. 2000. “Participation in Heterogeneous Communities.” _Quarterly Journal of Economics_ , 115(3): 847-904.

- Alesina, Alberto and Dani Rodrik. 1994. “Distributive Politics and Economic Growth.” _Quarterly Journal of Economics_ , 109(2): 465-90.

- Araujo, M. Caridad, Francisco H. G. Fereira, Peter Lanjouw, and Berk Özler. 2008. “Local Inequality and Project Choice : Theory and Evidence from Ecuador.” _Journal of Public Economics_ , 92(5-6), pp. 1022-46.

- Baird, Sarah, Craig McIntosh, and Berk Özler.
This paper seeks to assess how fuel subsidies to the Chinese distant water fleet, by far the largest in the world, have contributed to the steady depletion of global fish stocks. To this end, we use real-time tracking data on the fishing activity of more than 1,000 individual Chinese distant water fishing vessels over the period 2016 to 2020. We identify the effect of fuel subsidies on fishing behavior by exploiting China’s 2016 distant water fuel subsidy reform. This reform created 25 separate thresholds at which fuel subsidies discontinuously change for otherwise similar-sized vessels. The resulting regression discontinuity estimates are large: we find that a 1% increase in fuel subsidies increases hours of fishing by 2.2% and distance traveled by 1.7%. The local average treatment effect indicates that a $10,000 increase in fuel subsidy around discontinuity thresholds leads to 170 additional hours of fishing per month.

We then use these empirical estimates in a counterfactual analysis that isolates the contribution of Chinese distant water fuel subsidies to the overfishing status of global stocks. We find that reforming fuel subsidies to China’s distant water fishing fleet could reduce, but not entirely eliminate, overfishing in most FAO Ocean Regions. Furthermore, a 50% reduction in distant water fuel subsidies could lead to an average gain of 17% in steady state fish biomass across regions, indicating that reforming fuel subsidies is a promising strategy for rebuilding depleted fish stocks.

The results of this paper have key implications for ongoing debates about how to reform subsidies in fisheries. For example, in June 2022, the WTO adopted the “Agreement on Fisheries Subsidies”, which prohibits signatory countries from subsidizing vessels that fish for depleted stocks or engage in Illegal, Unauthorized, and Unregulated (IUU) fishing (WTO, 2022). This agreement is the second ever reached, on any subject, at the WTO. China has already updated its distant water fuel subsidy to comply with the WTO agreement (Ministry of Agriculture and Rural Affairs and Ministry of Finance, 2021a).
ate lag order. The international Stress scenario based on the actual historical cross-country crisis experience is presented under the “Stress Int.” heading. In addition, we compute the corresponding probabilities of the “Stress Int.” values vis-a-vis the VAR(1) model forecast distributions, and report them in the last column of Table 3 under the Pr(Stress Int.) heading.<sup>40</sup> The bank level balance sheet data are as of 2010:Q4.

Notice from Table 3 that under the TTC, PIT and Stress VAR scenarios the change in the nominal exchange rate is set to 0%. This is due to the fact that the economy operates under a fixed exchange rate regime vis-a-vis the Euro, and we assume that the current conversion rate to the EUR is sustained under the TTC, PIT and country specific VAR Stress scenario. Nevertheless, since there exists ample historical evidence suggesting that rate break down or are abandoned the authorities severe exchange pegs by during financial crisis periods (Reinhart and Rogoff, 2004; W ̈alti, 2005) we also consider a 31.5% depreciation of the local currency relative to the Euro under the “Stress Int.” scenario. The magnitude of this depreciation was determined from historical cross-country data as described in Section 3.3.

# **7.1.2. Bank Level Data**

The construction of the idiosyncratic (bank specific) risk component as described in Section 4 requires bank level data on credit growth at the peak of the last credit cycle or the most recent period of positive credit growth, and if available, data on the share of foreign currency denominated lending (SFX). Table 4 below shows summary statistics of annual credit growth in each asset class across the considered ten banks as of end-2007. The year

> 40These probabilities were constructed by taking the values under the “Stress Int.” heading in Table 3 and plugging them into the four step-ahead multivariate normal forecast density of the VAR(1)
# **5. Conclusion**

Unlike early pilots, the scaling up of weather index insurance in India has received little attention by academic economists. This paper offers an overview of the market and suggestions for future research and innovations.

First, states may wish to support long-term development of improved products that aim to minimize basis risk. A comparative statistical analysis of different products would be valuable and should be possible in India given the availability of long-term yield and weather data. This could lead to further standardization of products, based on agronomic and statistical principles, which would in turn support robust actuarial design and pricing. In addition to this analysis, the current market structure, under which insurers propose both products and prices, may not offer appropriate incentives to insurers to invest in developing improved products since innovations can be copied by rival insurers. Instead, states may wish to consider tendering in two phases, first with a multipleyear contract to design products and second with a one-season contract to sell products, or to commit to multiple-year contracts with insurance providers.

Second, governments may wish to enhance consumer protection legislation for indexed insurance products, for example, by requiring that any contract states historic burn costs for at least the last ten years. This could help farmers decide whether a particular product is good value, and whether it could be relied on to pay out in bad years.

Third, the WBCIS offers substantial opportunities to understand how to increase demand, particularly from the most vulnerable farmers. A rigorous monitoring and evaluation could be integrated into these programs to ensure that at the end of the pilot period government and states have the information they need to make decisions about the future of agricultural insurance in India.

Fourth, while this paper has focused on weather indexed insurance contracts, there is merit in further research to better understand how best to combine the information from different indices so that farmers can rely on timely claim payments in bad years. For example, an early part-settlement based on a weather index could be combined with a final
# **Figure 3**

Trends in Import Penetration by Source <!-- Start of picture text --> 1975 1980 1985 1990 1995 2000<br>Import Penetration (IP) IP: Low Income Country Share<br>0.20<br>0.15<br>0.10<br>Import Penetration<br>0.05<br>0.00<br><!-- End of picture text --> _Notes_ : Import penetration trends by source are taken from Bernard et al. (2006), and are calculated as a share of the total product market. The industry level data are aggregated to annual averages using industry employment weights taken from the CPS MORG.
As discussed earlier, our aquifer dataset consists of four types of aquifers. In our simulations, we focus on groundwater supply through local shallow aquifers, due to their lower costs and relatively higher recharge rates than other aquifer types.

In Table 2, we present the per-capita concentration of local shallow water aquifers in the African context compared with other aquifer types. SSA has the highest share of local shallow aquifers among all regions worldwide (see Figure 1).

Furthermore, we observe that grids predominantly composed of local shallow aquifers are distributed across the region (see Figure 3). Comparing these areas with recharge rates (see Figure 2a) reveals that regions with a high share of local shallow aquifers also coincide with high recharge rates, supporting the earlier observation of sustainable recharge in these aquifers. Similarly, comparing groundwater depths (see Figure 2b), we find that regions with low groundwater depth align with areas predominantly containing local shallow aquifers.

One potential concern regarding local shallow aquifers is their lower productivity relative to major alluvial aquifers, which may make them unsuitable for irrigation purposes. Therefore, we map aquifer productivity across SSA (see Figure 2c) and find only a few regions have aquifer productivity below low to moderate levels. This observation suggests the aquifers in the region can, at the very least, support small-scale irrigation.

Given the availability of local shallow aquifers and their ease of access, the possibility of using them sustainably for irrigation presents an opportunity for African farmers.

## **4.2 Agriculture in Sub-Saharan Africa**

This paper aims to explore the potential increase in agricultural production and productivity that can be achieved through groundwater irrigation. Currently, only 6.4% of the GAEZ grids have irrigated croplands. However, by examining the descriptive statistics presented in Figure 4, we can observe that although the harvest area of irrigated grids has remained constant over the years, the productivity, measured in terms of crop yields, of these irrigated croplands is nearly three times higher, on average, than that of rain-fed
# **5.3.Technical Efficiency, Potential Gains, and Economic Implications**

Technical efficiency scores, representing the distance from the potential production frontier, are computed in accordance with Equation 6. Figure 4 presents the distribution of technical efficiency scores under each suitability class for both MAPS- and AFSIS-based suitability classifications while Table 6 summarizes the scores, the potential production (in kilograms), and the potential yields (kilograms/hectare). As with the stochastic frontier analysis, AFSIS results are only reported for the overall sample and S2 classification (see Annex Table A5 for technical efficiency scores of S1 and S3 classes).

Farmers cultivating MAPS-based S3 plots exhibit the highest technical efficiency scores, indicating they are operating most closely to the production frontier given their soil suitability. Farmers cultivating S1 and S2 plots have higher realized yields and lower technical efficiency scores, suggesting they have the potential to achieve the most significant gains in their maize yields vis-a-vis their already superior observed levels. T-tests reveal that while the technical efficiency scores of S1 and S2 plots are not different to a statistically significant degree, the scores between S1 and S3, and between S2 and S3, are statistically different at the 10 percent level. The resulting potential output per plot and potential output per hectare are significantly different at the 1 percent level, between all classifications.

The gap between mean realized yield and potential yield is 1,394 kg/ha, or 86 percent of the realized mean yield, on S1 plots. S2 plots only have the potential to increase yields by 69 percent on average, while S3 plots are constrained to 59 percent yield growth from the 828 kg/ha realized average. Figure 5 presents the potential yield gains for each of the MAPS-based classifications and AFSIS S2 plots, illustrating the greater level and percentage increase in potential yields for more highly suitable plots.

_Table 6_ . Technical Efficiency and Productivity Potential

|||**MA**|**PS**||**AF**|**SIS
making in selecting benefits over employment, or in selecting employment in the informal sector over that in the formal sector. These include the fact that (i) access to benefits is dependent on income earned, (ii) in some programs, if a household member is working in the formal sector s/he is disqualified from eligibility, and (iii) benefits are removed in their entirety should income rise above a relatively low threshold. An important consideration in determining whether these impacts are likely to be sizable is the value of benefits provided. Notwithstanding the rapid increases in spending over the past decade, the overall value of benefits provided to households remains low. Figure 1 uses survey data from Turkey and other comparator countries to estimate the value of benefits as a percent of pre-transfer household income. For Turkey, the value of benefits is 7
After aggregating plot-level survey data on manure application and cash crop cultivation to the parcel level,<sup>13</sup> we estimate: i i i i (1) where i is defined above; controls for unobserved household fixed effects such as risk-bearing capacity that do not vary across parcels;ii i is a vector of parcel-specific indicators of land rights for those who YY αα report or are reported as land owners; i is a vector of parcel controls including size, soil type, slope, and RR location in a swamp/wetland;<sup>14</sup> and i is an error term. As public programs including input subsidies XX (Asfaw _et al._ 2017) are administered by any one of Malawi’s 28 districts, we cluster errors at the districtεε level and use the wild cluster bootstrap (Roodman _et al._ 2018) to account for the small number of clusters. The coefficient of interest is _β_ , the vector of coefficients capturing the effect of different types of land rights on land-attached agricultural investment. To allow for heterogeneity of effects by gender, we denote women’s and men’s land rights by i and i , ff mm respectively. i is an indicator variable for women’s land rights, equaling one if at least one woman in RR RR ff household _j_ reports (or, in the case of IHS4 data are reported as) having land rights of a certain type to RR parcel _i_ . Similarly, i is the equivalent indicator variable for men’s land rights that equals one if at least mm one man in household _j_ has rights of a certain nature to parcel _i_ . We modify (1) as follows RR i 1 i 2 i i i (2) ff mm ii where and YY = αα + ββ RR + ββ RR + γγXX + εε + i 1 i 2 i i i (2) where 1 and 2 are estimated effects of land rights being held by women or men, respectively
|(0.121)|(0.281)|(0.163)|
|Bandwidth|0.199|0.148|0.310|0.270|0.199|0.271|0.259|
|N|1,500|1,020|8,472|6,912|79|89|87|
|Binding vessels|X|X|||X|X|X|
|Non-binding vessels|||X|X||||
|Impose bandwidth|X||X||X|||
|Mean dep. var.|0.700|0.686|0.803|0.809|0.316|0.562|0.165|

Notes: All regressions use default options from the rdrobust package and control for gear fixed effects, gross tonnage, length in meters, and engine horsepower. Mean dep. var. is the mean dependent variable within the bandwidth. In Columns 1 to 4, the unit of observation is a vessel-month-year, we exclude vessels built on or after 2016, the dependent variable is an indicator for positive fishing hours, and we cluster standard errors at the vessel-level. In Columns 5 to 7, the unit of observation is a vessel and standard errors are heteroskedasticityrobust nearest neighbor. The dependent variables in Column 5 to 7 are indicators that equal 1 for vessels built on or after 2016, if the first year we observe a vessel in the GFW data is not 2016, and if the last year we observe a vessel in the GFW data is not 2020. In Columns 1, 3, and 5, the bandwidth is from Columns 1, 2, and 1 (again) of Table 3, respectively. In Columns 2, 4, 6, and 7, the bandwidth selection procedure is mean-squared error optimal.

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