rafmacalaba/data-use-mentions-extended huggingface dataset:
on skills of the Indian engineering graduates. For instance, the NASSCOM and McKinsey report (2005) finds that 75% of engineering graduates are not employable by multinational companies.

Obviously, this (dis-)satisfaction level is an average. There is no doubt that India produces a large number of exceptionally bright engineers, as can be seen in the importance of hightech entrepreneurs in the US that are of Indian origin, (Wadhwa, Rissing, Saxenian, and Gereffi, 2007) and the share of international publications that come from Indian engineering education Institutions. Also, it could be that employers have unrealistically high expectations regarding the potential skill level of graduates. In the US survey of engineering graduates, the average satisfaction rate was 4.01 equivalent to ―very satisfied‖. While the Indo-US difference is likely to reflect a difference in the quality of the graduates‘ skills, it could also stem from more reasonable expectations from employers or a generally more positive attitude of US employers. One should be cautious in directly comparing subjective surveys internationally.<sup>12</sup>

> 12 Satisfaction level of employers toward graduates‘ skills is not always pessimistic, at least in other countries.  Several similar employer surveys in the US, for instance, show that employers are satisfied, in some cases very satisfied, with graduates‘ skills. The employer survey conducted by Noel-Levitz and Utah State University shows that employers are very satisfied with the graduates from Utah State University. They show the interest to continue to hire the graduates. Another employer survey conducted by the University of Texas-Pan American also shows that very few employers are dissatisfied with graduates‘ skills. Given the different contexts between the US and India, satisfaction level cannot be simply comparable between the two countries. However, employers do not always unrealistically evaluate their satisfaction level toward graduates‘ skills.

19
[0.000]|[0.001]|[0.31; 0.54]|
|Demographics|No|No|No|Yes|Yes|No|No|No|Yes|Yes|
|County FE|No|No|No|Yes|Yes|No|No|No|Yes|Yes|
|N(pooled waves)|33,826|21,703|55,529|52050|52,050|17,803|3161|20,964|20,482|20,482|

Note: All results are weighted using appropriate sampling weights. Column 4 for each variable conditions on demographic controls and county fixed effects. For employment, the work stoppage rate and hours worked, demographic controls include age, age squared, gender, educational attainment and whether the individual was the respondent. For income, demographic controls include age, gender and educational attainment of the household head as well as a dummy indicating poor flooring material.

15
our desire to assess changes in vulnerability.

income). We therefore use the high-income poverty lines of $24.35 per day. Note that this poverty line is the poverty line for high-income countries and that that all countries in the European Union except Bulgaria (an upper-middleincome country) are high-income countries. Another option would have been to use the upper-middle-income line for upper-middle-income countries. But as this value ($6.85) is substantially lower than the high-income line ($24.35), we use the same line for all countries. For a discussion of how the poverty line for high-income countries was established, see Joliffe and others (2022).

3 For symmetry with the lower threshold, we define the upper threshold also in terms of probability of falling into poverty – but at an even lower level (of 1 percent). Alternative definitions can be to use the income that puts a household in a certain top percentile of the income distribution. We discuss these alternative definitions in section 4 and table A.4.

4 Eurostat requires permission from each participating country to access microdata. Unfortunately, Germany does not authorize the access to microdata pertaining to that country for researchers that are based in non-EU institutions.

5 Switzerland is excluded because some years of data were not available.

6 The analysis excludes Croatia and Romania, because data for these countries are not available for the 2005-08 period.

7 As of 2024, there is not yet a 4-year panel that completely excludes the pandemic shock of 2020 and 2021.

7
_c_ = (5547 _/_ 87) _/_ (5899 _/_ 632) _≈_ 7.

The numbers obtained above are calculated from a highly aggregated budgetary figures. Therefore, we consider the generic, all-inclusive budget for a one year, 3,200-household living standards survey presented in Grosh and Mu ̃noz (1996, Table 8.2). While the numbers are hypothetical, they are meant to be used to create a prototype budget and may well serve our purpose to create a ballpark figure. We assume that each cluster has 16 households as with a majority of Living Standards Measurement Study (LSMS) surveys reviewed in Table 4.1 of Grosh and Mu ̃noz (1996), which implies that there are 200 clusters in this hypothetical survey.

There remain a challenge in deriving an estimate of _c_ because we need to assign each cost components to (i) variable costs proportionate to the number of clusters, (ii) variable costs proportionate to the number of households, and (iii) the fixed costs. We decided to assign all costs relating to “travel allowance” and vehicles, fuel, and car maintenance to (i) variable costs proportionate to the number of clusters, which amounts to $345,880 for 200 clusters. For (ii) variable costs proportionate to the number of households, we include all the costs relating to “base salaries” and printing of questionnaire as well as “materials” other than vehicles, fuel, and car maintenance, which in total amount to $475,150 for 3,200 households. The remaining costs including “consultancy and travel” and “other” are taken as (iii) fixed costs. Based on these classifications, an estimate of _c_ can be obtained as follows: _c_ = (345880 _/_ 200) _/_ (475150 _/_ 3200) _≈_ 12.

We have also collected similar budgetary information for several household surveys through personal communications. We then computed
citations made by each sanctioned field _n_ to field _j_<sup>19</sup> .

where _sanctionn,t_ is a dummy variable that equals 1 if the IPC field is sanctioned at time _t_ . The upstream and downstream measure allows us to estimate the significance of the sanctioned fields in the innovation process of indirectly exposed technology fields. For instance, if the semiconductor sector is sanctioned by the U.S., firms heavily dependent on semiconductor innovations (i.e. firms in the downstream sectors) may experience negative impacts due to the interruption of technology flows from the U.S.. Conversely, such sanctions may foster domestic innovations and increase the demand for inventions from the technology field that the semiconductor sector is dependent on (i.e. firms in the upstream sectors). The direction and magnitude of network spillover effects of U.S. sanctions are estimated using the following regressions: where _DummyDownstreamj,t_ is a dummy variable that equals 1 for firms in the downstream sector (i.e. its downstream measure is larger than upstream measure). Table 11 shows the estimates of equation (6) in columns (1) to (3) and the estimates of equation (7) in columns (4) to (6). These estimates are largely consistent with our hypothesis. Firms in sectors that are located downstream to the sanctioned sectors experience a significant decline in patenting, whereas firms in sectors that upstream to the sanctioned sectors increase their

> 19As citation data is not often consistently available from China patent office and other regional patent offices, when measuring IPC-to-IPC citations, we only consider patents that were applied for in EPO, USPTO, or WIPO, where the citation information is complete and consistent over time

24
||||||
|Number of toilets in compound|No.|2281|1.53|1.00|1.18|
|Current toilet type [7]||||||
|Pour-fush|%|2287|36.99|-|48.29|
|Pit latrine|%|2287|59.34|-|49.13|
|Monthly expenditure on sanitation [8]|US$|1133|8.19|5.83|15.27|

_Notes_ . 2016 property owner baseline survey data used except for "Water payment practices" which uses March 2016 utility billing data. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated. [1] Includes some secondary education or higher. [2] Estimated as rental income divided by the percentage rent of total income. [3] Includes property that is received for free or inherited with a mortgage paid off. [4] Assumes 92% reduction factor from dwellings to households, and assumes households equals 1 if property owner resides in the compound and does not rent out dwellings. [5] Includes actual rental income received. When including all rental that was not received for the month prior to the survey, the average monthly rental income would be US$ 134.42. [6] Using billing and payment data as of March 2016. [7] Options do not sum to 100% as some compounds have multiple types. [8] Conditional on having a pit latrine.

30
# **1 Introduction**

The information that public officials use to make decisions determines the distribution of public resources and the efficacy of public policy (Callen et al., 2018; Dal Bó et al., 2018). Errors in that information can reduce social welfare as the marginal efficiency of public investments falls. Correspondingly, policyrelated research frequently attempts to improve the information set of public officials and thereby raise the efficiency of policy.<sup>1</sup> Despite the importance of bureaucratic beliefs for citizen welfare, there is an absence of empirical evidence on the accuracy of information used in public sector decision-making. This paper develops a measurement framework for assessing the accuracy of a set of fundamental bureaucratic beliefs and provides descriptive evidence on their determinants. We then provide experimental evidence on the possibility of improving the accuracy of those beliefs through the provision of evidence briefings, akin to those used by actors inside and outside the public service to inform public officials.

More precisely, we develop a survey that extracts the beliefs of public officials about the key variables on which their work depends, such as the size of the population they are serving or sector-specific variables such as the number of primary-age children who are in school. We compare the responses of the surveyed officials to objective benchmark data from administrative and survey data to calculate the scale of errors that these officials make. We thus provide among the first large-scale descriptive evidence on the nature and correlates of information in the public sector.

We combine these measures with information about the structure of the hierarchy in which public officials work and the management practices they operate under to show that our descriptive results closely mirror a series of core theoretical predictions regarding the distribution of information in the public sector. Information acquisition in bureaucratic hierarchy is theoretically distinctive to models of learning by autonomous agents. Unlike markets, bureaucracies do not have organic information creation mechanisms, such that individual officials must undertake costly actions to acquire and absorb information. As bureaucratic decision-making is based on team production and decision-making, officials
|Tanga|Coastal|5.05|0.385|
|---|---|---|---|
|Shinyanga|Central|5.70|0.364|
|Singida|Central|6.27|0.432|
|Manyara|Central|6.87|0.531|
|Tabora|Central|8.37|0.754|

Source: Author‟s calculations using HBS 2007 data.

An important route out of poverty for maize growers is the option of applying the available factors of production to an activity other than producing maize<sup>21</sup> . With her land, labor, capital and knowledge, a maize grower may choose to grow an alternative high value crop such as sunflowers or other horticultural produce. The decision to do so would entail a shift for the household from producing their staple food to purchasing it. However, in the absence of a secure mechanism for purchasing food within the village, the risk of facing food insecurity may exceed the premium that could be attained from switching to higher value crops. The farmer is effectively locked in a cycle of food production for subsistence that prevents their entry into more profitable markets.

# **6.0 Concluding remarks**

In the post reform period, farm-gate buying has emerged as the most prevalent mechanism for marketing maize in many regions of Tanzania. In this paper, we estimate the transaction costs associated with farm-gate buying, specifically search and information costs, using a measure of household remoteness (distance to market) as a proxy. We showed that the dispersion of maize producers and the farm-gate buying system make transaction costs such as search and village to town transport particularly widen the price gap, causing a downwards pressure on producer prices. We also showed that since non local buyers with limited information about the local market enter and search for maize more actively when maize is abundant, buyer search costs incurred are likely to be higher in the harvest
30. Jones, R. L., Guha-Sapir, D. & Tubeuf, S. Human and economic impacts of natural disasters: can we trust the global data? _Sci. Data_ **9** , 572 (2022).

31. Rice, X. Severe drought causes hunger for 10 million in west Africa. _The Guardian_ (2010).

32. World Bank. Niger Takes Action to Reverse 50 Years of Food Insecurity. _World Bank News_

- https://www.worldbank.org/en/news/feature/2011/09/01/niger-takes-action-to-reverse-50-yearsof-food-insecurity (2011).

33. Munro, T. & Wild, L. As drought hits Ethiopia again, food aid risks breaking resilience. _The Guardian_ (2016).

34. Jeffrey, J. Ethiopia drought: How can we let this happen again? _Al Jazeera_ (2016).

35. Chonghaile, C. N. Malawi floods devastation far worse than first thought. _The Guardian_ (2015).

36. Africa drought fears grip Malawi and Mozambique. _BBC News_ (2016).

37. Gall, M., Borden, K. A. & Cutter, S. L. When Do Losses Count?: Six Fallacies of Natural Hazards Loss Data. _Bull. Am. Meteorol. Soc._ **90** , 799–810 (2009).

38. Moriyama, K., Sasaki, D., Ono, Y., & International Research Institute of Disaster Science (IRIDeS), Tohoku University 468-1-S302 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-0845, Japan.

- Comparison of Global Databases for Disaster Loss and Damage Data. _J. Disaster Res._ **13** , 1007–1014 (2018).

39. Panwar, V. & Sen, S. Disaster Damage Records of EM-DAT and DesInventar: A Systematic Comparison. _Econ. Disasters Clim. Change_ **4** , 295–317 (2020).

40. Wirtz
Policy Research Working Paper 5935

# **Abstract**

This paper uses a novel dataset on United States food import refusals to show that reputation is an important factor in the enforcement of sanitary and phytosanitary measures. The strongest reputation effect comes from a country’s own history of compliance in relation to a particular product. The odds of at least one import refusal in the current year increase by more than 300 percent if there was a refusal in the preceding year, after controlling for other factors. However, the data are also suggestive of the existence of two sets of spillovers. First, import refusals are less likely if there is an established history of compliance in relation to other goods in the same sector. Second, an established history of compliance in relation to the same product by neighboring countries also helps reduce the number of import refusals. These findings have important policy implications for exporters of agricultural products, especially in middle-income countries. In particular, they highlight the importance of a comprehensive approach to upgrading standards systems, focusing on sectors rather than individual products, as well as the possible benefits that can come from regional cooperation in building sanitary and phytosanitary compliance capacity.

This paper is a product of the Growth and Competitiveness Unit, World Bank Institute. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Jmaur@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent
26 These water transfers initiated by large farmers using mostly their own capital (some farmers have also obtained bank loans) also benefits smaller farms in the vicinity through periodic water markets (i.e., during water scarce time) operating under two arrangements: one involving the sale of water at Rs. 20/hour and the other involving a somewhat a long-term arrangement. Under the latter arrangement the buyer has to deposit a refundable amount of Rs. 10,000/acre with the seller who, in turn, supplies water in lieu of interest payment (since the monthly interest in the village varies from 2 to 10 per cent, water payment amounts to Rs. 2400 to 1200/acre/year. This higher amount is not a problem as the buyer also grows high value crops like banana.) Notably, since these water marketing activities occur essentially in scarce water periods, they remain latent in periods with better rainfall and water supply conditions.

# **_Transferable Water Entitlements In Australia_**

Transferable water entitlements were introduced in several Australian states--in New South Wales, and South Australia in the early 1980s, and in the late 1980s in Queensland and Victoria. Data in Pigram et al. (1992) suggests a positive trend in quantities of water trade in all states over the period 1987-1988. The volumes transferred accounted for 5- 10% of the water use in these states. It should be emphasized that most water transfers were not permanent, but seasonal; of all transaction, 620 were of a temporary nature and 46 were of a permanent nature. Total volume transferred temporarily was 137.3 million **m**<sup>**3**</sup> **,** and total volume transferred permanently was 19.7 million **m**<sup>**3**</sup> **.** The data for the New South Wales state, which exists between 1984 and 1991 is summarized in Table
22. Using a 10-point scale where 1 represents “not a serious problem at all” and 10 represents “an extremely serious problem”, respondents were asked how serious a problem they considered climate change to be: as Figure 7 shows, average scores by respondents in Kazakhstan (5.7) and Tajikistan (4.7) were considerably lower than in EU17 countries (7.9).<sup>15</sup>

**Figure 7: Seriousness of climate change problem (1-10 point scale)**

<!-- Start of picture text --> EU27 countries 7.9<br>Kazakhstan 5.7<br>Tajikistan 4.7<br>0 2 4 6 8 10<br>Average score (1‐10 scale)<br><!-- End of picture text --> Source: Authors’ calculations based on P-LITS data; Special EU Barometer 300/wave 69.2 23. Within Tajikistan, average ratings of the seriousness of the climate change problem vary quite a bit across different groups. Amongst those reporting themselves as being “very well informed” about the consequences of climate change, average scores were considerably higher than amongst those that were “not at all informed” (5.7 vs. 3.3 respectively). Similarly, average scores among respondents in Dushanbe (6.0), those reporting sale/barter of farm products as their main source of income (5.3), and the poorest one-third (5.0) were also higher than the rest of the population.

**Table 3: Ordered Probit Results**

|**Respondent Characteristics**|**Coefficient**|**Std. Err.**|**z**|**P>z**|
|---|---|---|---|---|
|**1. Income group:*
and 2016/17 which exacerbated preexisting vulnerabilities in the Somali population. Both conflict and drought have led to large‐scale internal displacement (World Bank, 2018a). The recent 2016/17 drought led to the displacement of approximately one million Somalis, adding to an existing population of internally displaced persons of 1.1 million (UNHCR, 2018).

As is typical for fragile states, Somalia is highly data‐deprived, leaving policy makers to operate in a statistical vacuum (Beegle et al., 2016). Specifically, years of civil war and ongoing conflict have eroded Somalia’s statistical infrastructure and capacity, leading to the lack of key macro‐ and micro‐economic indicators, including the poverty rate (Hoogeveen and Nguyen, 2017). The government conducted and published the last full population census in 1975, while Somalia Socioeconomic Survey of 2002 was the last country‐wide household survey (UNFPA, 2014). Most recent existing data sources are local FSNAU and FAO food and nutrition surveys, while organizations operating within Somalia implemented a range of smaller surveys. In 2014, UNFPA implemented the first nationwide Population Estimation Survey (PESS) in preparation for a national census, finding the total population to be 12.3 million, of which 42 percent are urban, 23 percent rural, 26 percent nomadic, and 9 percent are internally displaced (UNFPA, 2014).

Funded by the World Bank, Somaliland carried out a household budget survey (SLHS) in 2013, which generated much‐needed indicators, including poverty estimates, but the sample was not representative especially for the rural population and did not cover the nomadic and displaced populations. The World Bank conducted the first wave of the Somali High Frequency Survey (SHFS) in the spring of 2016, representative of the accessible urban, rural, and IDP population in 9 of 18 prewar regions as well as

> 2 Somaliland self‐declared independence in 1991.

> 3 See the Armed Conflict Location and Events Database (ACLED), Somalia, for
mNAIS), which was piloted during the spring (Rabi) season 2010-11. The approach uses long term data series, available in India, to calculate premium rates that are stable yet reflective of regional differences and responsive to changes in risk over time. This enhanced risk classification increases the equity of the mNAIS as compared to its predecessor, the NAIS, and helps to protect the crop insurance portfolio from adverse selection.

In the Indian context the development of this design and ratemaking methodology had much wider implications than just improving the current crop insurance scheme; it has allowed the Government of India to transition to a market-based crop insurance program with involvement from private sector insurers and reinsurers, improved fiscal management for government by enabling ex-ante budgeting for government subsidies towards premium, and increased equity and faster claim settlement for farmers. While the current paper is targeted at a technical actuarial audience, we refer readers interested in the wider policy agenda to Mahul et al. (2011).

While the product details are specific to India, the principles can be applied much more generally to indexed insurance products in low and middle income countries. However, while we hope that others will find this paper useful, it does not substitute for the expert advice of an appointed actuary.

# _The Modified NAIS_

At the time of writing the modified NAIS (mNAIS) is in a pilot phase and may be amended in the future. In this paper the following structure for mNAIS is assumed:

- The primary benefit to farmers from mNAIS is calculated on an area-yield-based approach: if the observed seasonal area-yield per hectare of the insured crop for the defined geographical area (the _insurance unit_ ) falls below a specific threshold yield, all insured

2