rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5.1  Fish Species Change Scenarios for** **_Upazila_ s in the Sundarbans Region**

For each of the 83 species identified in Appendix Table A1, we build a digital map for 2012 that assigns 1 to pixels that satisfy the species’ stable habitat criterion (pixel salinity range falls within the species’ tolerance range) and 0 otherwise. We add across the 83 maps to determine total species with stable habitat in each of 101,600 pixels. Then we perform the same operations for all 27 salinity scenarios in 2050 and calculate percent changes (2012-2050) in total species for each pixel. Overlaying an administrative map shapefile provided by the Government of Bangladesh, we compute mean percent changes in the 27 scenarios for 110 _upazila_ s in the Sundarbans region.

# **5.2  Poverty Incidence in the Sundarbans Region**

We assess collective risk using estimated total poverty populations for _upazila_ s in 2011. These are the product of 2010 poverty incidence estimates provided by the World Bank (2014b) and 2011 population estimates from the Bangladesh Bureau of Statistics. Following World Bank (2014a), we use two standards to determine poverty incidence: the upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics;<sup>15</sup> and the lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line.

# **5.3  Risk Assessment for** **_Upazila_ s in the Sundarbans Region**

To illustrate the range of results produced by this exercise, we employ the two bounding scenarios for 2050 that are mapped at the pixel level in Figure 1: least change (Scenario B1, GCM MIROC-3.2, SLR 27 cm, land subsidence 2 mm/year); and most change (Scenario A2, GCM

> few coastal fish species may emerge gradually in inland water but their harvesting technology is costly and not affordable to the poor.
Central and Eastern European emerging economies and four other emerging economies was considered by Oskooee, Kutan and Xi (2013). Results confirmed that the uncertainty’s impact is transitory in most countries and money demand was correctly specified and stable in most countries.

Bahmani and Kutan (2010) tested the stability of money demand for Armenia, Bulgaria, the Czech Republic, Hungary, Poland, the Russian Federation, and the Slovak Republic. They used the bounds testing approach to co-integration that shows stable money demand in these countries.

To address issues of data aggregation, Hsiao, Shen and Fujiki (2005) used Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. Aggregate data appear to support the contention that there was no stable money demand function, while disaggregate data showed that there was a stable money demand function. The diametrically opposite results are attributed to the neglected heterogeneity among micro units.

During the past five years, new modeling techniques have evolved for country level research related to money demand. Focus was directed to investigating the presence of a non-linear relationship between monetary aggregates and interest rate, as well as the possibility of considering time-varying behavior of coefficients.

Lee and Chang (2013) employed second-generation Random Coefficient (RC) modeling to investigate the time-varying behavior and the predictability of the money demand function in Taiwan, China, over the period 1982Q1 to 2006Q4. Empirical results indicated that the values of elasticity in the RC estimation are significantly different from those in other studies, because of the use of coefficient drivers that can make an association with real events occurring in Taiwan, China, such as the financial liberalization after 1989 and the Asian financial crisis of 1997–1998.

Bae and De Jong (2007) combined the logarithmic specification, which models liquidity better than a linear model, with the assumption that the interest rate itself is an integrated process. Results for the US produced larger coefficient estimates than previous research in addition to superior outof-sample prediction.

Recent research also challenged the conventional wisdom of instability of demand
suspicion with respect to the capabilities and intentions of the private sector has led to greater involvement of the nation‘s parastatal, Agricultural Development and Marketing Corporation (ADMARC), in maize marketing. Tschirley and Jayne (2010) conclude that market shortages and stock-outs at ADMARC have sometimes led to huge price surges. However, there is growing private sector entry in maize marketing and encouraging evidence on the number of traders to whom farmers can sell maize and proximity to point of sales.

Over the past few decades in Kenya, synergies between the liberalization of the input and maize markets and public investments led to tangible investments by the private sector in not only seed and fertilizer retailing but also maize marketing (Ariga and Jayne 2010). Maize marketing margins have also contracted, as well as the distance traveled to the point of maize sale. However, maize sales remain highly concentrated among farmers. The Tegemeo Institute panel data confirm that less than two percent of the farms account for 50 percent of the overall marketed maize surplus from the smallholder sector. Most smallholders, which account for 96 percent of all the farm households in Kenya, were consistently buyers of maize in the three seasons for which data were collected (which included one good production year and two average years).

Kenya has pursued a policy of high food prices with import tariffs in the range of 25-50 percent and until 2005, restrictions on maize inflows from neighboring countries. The operations of the maize marketing board (NCPB) have raised the level of maize prices in the country by offering support prices well above market levels (Jayne et al. 2008). Grain price supports and/or stabilization policies that raise mean price levels over time will have income distributional effects that run counter to stated goals of reducing poverty. Mean-neutral forms of price 18
*|...|...|...|...|
|**New LICs**|||||
|Syrian Arab Republic *|...|970|...|...|

Source: World Bank World Development Indicators, World Integrated Trade Statistics. Notes: Asterisks indicate economies affected by fragility, conflict, and violence (FCV). Ellipses indicate data unavailability. GNI per capita according to the World Bank Atlas method. GNI per capita for 1999 was published in August 2000 and reflects the original data used for country income classification in the 2001 World Bank fiscal year, while GNI per capita for 2018 was published in July 2019 and reflects the data used for the 2020 World Bank fiscal year. The LIC threshold is $1025 for the 2020 fiscal year. South Sudan is also a new LIC, but not included in the table because it only gained independence in 2011; data not available.

21
evaluations. For example, data on health programe costs would be needed to complement data on patient costs.

A standardised questionnaire would, of course, still pose its own problems. Flexibility in wording, for example, would be required to ensure the relevance oO the questions to each individual country. Perhaps it would be better to undertake "add-on" studies of particular issues. Such studies could then be made appropriate to each country's situation and needs, and could collect both patient data and complementary health system data.

Clearly, the cost (both monetary and in terms of time, disruption to present systems etc.) of any data collection process additional to (or instead of) those in existence, must be weighed against the possible benefits. Health service issues have been identified as the most appropriate focus for a WHS, in terms of the possibility and relevance of generating benefits (ie. improvements in economic evaluation methodology). It should also be considered whether the same improvements (and benefits) might be better generated through improving existing data collection processes.

# 4. SCOPE AND LIMITATIONS OF INTERVIEW-BASED SURVEYS

# 4.1 Health interview surveys - an introduction

There is a growing amount of experience in the use of health interview surveys in developing countries. These typically include questions on general morbidity, cause-specific morbidity, mortalitv, causes of death, nutritional status, coverage and use of services, knowledge of and attitudes towards health practices/services, and an array of explanatory or independent variables (age, sex, residence, socioeconomic indicators, maternal education etc.). There are a number of theoretical models which attempt to classify the 'explanatory var,ables', and the 'proximate determinants' which affect an array of 'outcome variables' (see, for example, Mosley and Chen 1984 and van Norren and van Vianen 1986). Here, we do not follow any particular model but classify morbidity, mortality and nutritional status as outcome variables, consider a range of 'process' variables such as 23
- 53 -

the Ministry of Labor, and are financed in a variety of ways beyond direct government budgets. Some provide broad national coverage for industrial training; others are restricted to a particular sector. Tn most cases formal vocational education remains a separate function administered by the Ministry of Education.

4.07 The status of these systems as reflected in the most recent Bank SARs or PCRs is summarized **in** Table 24. Changes in the longer-established systems may have occurred; in some cases systems are under development in current projects. The data thus reflect policies and objectives that have been **and** are being supported by Bank investments, not a necessarily accurate **and** up-to-date assessment of system configuration.

Table 24: The Development of National Training Systems

|ReglorVCoun|Year of<br>try Initiatio|n<br>Agency<br>N|ationa|l Sectoral<br>Fiiancing Policy|
|---|---|---|---|---|
|El Salvador|1978|Industrlal Training System<br>(not Inplemented)|X|Payroll Tax Levy (not<br>lrplemented)|
|||CENAP<br>- National Productivity<br>Center|X||
|Dominican<br>Reptbdlc|1979|INFOTEP|X|1X Payroll Tax Levy|
|Barbados|1979|National Training Board|X|O.5X Payroll Tax Levy|
|Brazil|1976|National Vocational Tralning<br>System|X|1X Payroll Tax Levy|
|Paraguay|1971|SNPP|X|1X Payroll Tax Levy|
|Uruguay|1983|COCAP<br>- Natlonal Vocational<br>TralnIng Council|X|Government<br>FInancing<br>Student fees cover all<br>recurrent costs|
|23%|
|Mexico|42%|<br>13%|35%|19%|10%|4%|12%|7%|
|Nicaragua|42%|19%|29%|19%|9%|6%|20%|12%|
|Paraguay|68%|<br>22%|3%|2%|25%|19%|4%|3%|
|Peru|8%|<br>6%|80%|33%|7%|5%|5%|4%|
|Panama|31%|<br>16%|19%|10%|13%|6%|38%|23%|
|Uruguay|98%|58%|2%|2%|0%|0%|0%|0%|
|Venezuela|48%|<br>19%|17%|6%|22%|10%|13%|4%|
|_LCR Totals_|_48%_|<br>_20%_|_32%_|_14%_|_10%_|_6%_|_11%_|_5%_|

Sources: 1) Population and density are from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” include GAEZ classifications marginal, very marginal and not suitable.
with two-part models in the literature (for instance, see Cragg 1971; Dow and Norton 2003; Heckman 1976; Nargis et al. 2013).<sup>7</sup>

# **a. The probability model and the proportion of entrants**

The proposed model exploits the variability of either prices or a welfare variable at the household level (such as consumption) to estimate a probit model of to estimate demand.<sup>8</sup> Assuming independent and normally distributed error terms, and because of a lack of accurate price data in the observations of individuals in household surveys, the probit model is written as follows:<sup>9</sup> P P h h), (1) h,ii �dd �= ff(log (ww ), XX where h denotes a welfare measure (such as income) for household _h_ , and corresponds to a binary variable taking the value of 1 if household _h_ reports positive consumption in good or service _i_ , h,ii ww dd or 0 otherwise. For the sake of simplicity, all explanatory variables are denoted with ; the h and indicators that identify households and goods or services are omitted; and welfare is referred to as XX ii income in the rest of this section, as follows: P P , (2) (dd= 1|XX) = Φ(XX′ββ)

or, alternatively, as follows: where Φ(. ) denotes the normal cumulative distribution function (CDF). Then, the probabilistic elasticity is given by the following: P where I���������� and P�������P refer to the average welfare and average probability of positive consumption at the population level (individual of reference), respectively. IIPPIIuu Taking derivatives and applying the chain rule on the probit model specification, it is possible to rewrite equation (4) as follows:<sup>10</sup>
improvement in developing countries’ policies. Better macroeconomic policies and greater openness have improved the economic environments of these countries. Inflation fell significantly during the 1990s, macroeconomic management improved, exchange rates were more stable, and trade barriers were reduced. But on the other hand, there was little progress in governance, controlling corruption, and the quality of institutions worldwide—as measured for instance by the governance component of the World Bank’s CPIA index—and significant variation across countries. As a result, adjustment was, by and large, a failure. None of the top 20 recipients of repeated adjustment lending over 1980-99 were able to achieve reasonable growth and contain all policy distortions (Easterly 1985). About half of the adjustment loan recipients showed severe macroeconomic distortions regardless of cumulative adjustment loans. Adjustment lending has almost no positive effect on economic growth and little on living standards. This is a conclusion reached by several internal World Bank (OPCS) reports.<sup>7</sup> Despite good intentions, adjustment lending by the World Bank does not have a good record. To be sure, it includes a number of success stories. But it also includes cases where adjustment programs were misguided or simply not followed. Heavy reliance on conditionality was usually ineffective in the sense that a large number of conditions did nothing to strengthen borrower “ownership” of the reforms. While the focus on improving poor policies was understandable, the design of adjustment programs paid inadequate attention to governance and institutional problems, which were major constraints on the investment climate. Moreover, there was little emphasis on mitigating the social costs of adjustment and even less on equity issues. This lack of specific attention to poverty issues, understandably, attracted heavy criticism.

Although conditionality can support policy changes, it cannot persuade reluctant reformers. The number and frequency of conditions attached to adjustment loans and standby agreements from international financial institutions have little to do with successful policy reforms. Case studies have shown that effective policy reform generally emerges from domestic political consensus— and
in sectors' contribution to overall growth, their labor intensity, and the welfare level of individuals employed in different sectors. Second, we include specific conditions to mimic the evolution of non-labor income transfers, with a particular focus on the changes in the real value of the transfers from the main anti-poverty program in the country. Finally, we account for the differential price trends across different good categories and their impact on the effective inflation rate households experience given their consumption basket. We implement this modeling in the case of Pakistan and show how it captures how the differential sectoral growth rates, the loss in purchasing power of public transfers, and the inflation gap between urban and rural areas all together impact the evolution of poverty since the last official measure in FY19 was published. The model is specifically tailored to the conditions of Pakistan, but its methodological approach offers complete flexibility to extend it to other country-specific conditions in terms of relevant economic sectors, non-labor income components, and available inflation information.

Our results are aligned with past evidence on how accounting for the distributional effects of growth produces more accurate predictions of the evolution of poverty (Caruso et al., 2017;

> 1 In Pakistan, the fiscal year covers the 12-month period from July to June of the next year. For example, FY19 covers the period from July 2018 to June 2019 inclusive.

> 2 Nowcasting refers to the practice of using recently published data to update key economic indicators that are published with a significant lag. Poverty nowcasting relies on actual GDP data, its accuracy depends only on the features of each nowcasting method (reference). Forecasting refers to the practice of predicting what will happen in the future by taking into consideration events in the past and present.

2
*The indicators must be available from available data sources** . Fortunately, a series of household surveys for Côte d’Ivoire have been administered since 1985. The surveys are nationally representative and fairly comparable over time. In addition to the long time period covered, analysts can take advantage of the information on household income and spending, which are usually not available in other surveys used for the HOI study of African countries.

- (ii) **The indicators chosen must be relevant for Côte d’Ivoire and provide useful information** . In terms of relevance, the broad categories of basic opportunities identified in earlier work (education and access to basic services) correlate to capabilities that are almost universally accepted as necessary for a productive life. However, in defining the precise indicators within each broad category, country-specific factors need to be taken into account. In particular, it is

10
# **References**

Ayers, J.C., Goodbred, S., George, G., Fry, D., Benneyworth, L., Hornberger, G., Roy, K., Karim, M.R., Akter, F. (2016) Sources of salinity and arsenic in groundwater in southwest Bangladesh. Geochemical Transactions 17, 1-22.

- BBS, (2015) Population and housing census 2011, Socio-economic and demographic report, National Report, vol. 4. Bangladesh Bureau of Statistics (BBS) Dhaka.

- BBS and UNICEF, (2010) Multiple Indicator Cluster Survey (MICS) 2009, Volume I: Technical Report, Progotir Pathey. Bangladesh Bureau of Statistics (BBS) and UNICEF Bangladesh.

BBS/WFP/IFAD, (2012) Undernutrition Maps of Bangladesh 2012. Bangladesh Bureau of Statistics (BBS), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), Dhaka, Bangladesh, p. 24.

- BGS and DPHE, (2001) Arsenic Contamination of Groundwater in Bangladesh, Vol. 2. Final Report  WC/00/19, in: Kinniburgh, D.G., Smedley, P.L. (Eds.), National Hydrochemical Survey. British Geological Survey (BGS) and Bangladesh Department of Public Health Engineering (DPHE), Keyworth.

- Bhattacharya, P., Hossain, M., Rahman, S.N., Robinson, C., Nath, B., Rahman, M., Islam, M.M., Brömssen, M.V., Ahmed, K.M., Jacks, G., Chowdhury, D., Rahman, M., Jakariya, M., Persson, L.Å., Vahter, M. (2011) Temporal and seasonal variability of arsenic in drinking water wells in Matlab, southeastern Bangladesh: A preliminary evaluation on the basis of a 4 year study. Journal of Environmental Science and Health,
boundaries to calculate geometry-based shape metrics with an open source library written in Python 2.5 for ESRI’s ArcGIS, The Shape Metrics Tool, described in Angel et al. (2010). Contrary to other studies, she finds that more compact cities (in terms of the geometry of the urban extent as opposed to population density) are characterized by larger populations, lower wages, and higher housing rents. This suggests that a city’s residents value compactness as a consumption amenity. By contrast, firms do not appear to be directly affected by city shape in their location choices and there is no evidence of a significant impact on the productivity of firms.<sup>2</sup> Most recently, in their analysis of urban development patterns of Indian cities, Tewari, Alder and Roberts (2016) find that there is a robust and positive relationship between a city’s initial level of

> 1 A similar phenomenon has occurred with the use of remote sensing data by economists.

> 2 Lall, Shalizi and Deichmann (2003) found similar results from an analysis of agglomeration economies and productivity in Indian industries. The authors find that benefits for firms to locate in dense urban areas do not appear to offset the associated costs.
Ensuring that some data is available most of the time periods is more important than ensuring that most of the data is available in some of the time periods. In addition, predictions are more reliable in high inflation episodes and less reliable in high volatility episodes, so data gathering processes may also use statistical methods to produce real-time price estimates and use the results in turn to determine how much new data gathering is needed.

# **References**

- Ahumada, H. and Cornejo, M. (2016). Forecasting food prices: The case of corn, soybeans and wheat. _International Journal of Forecasting_ , 32(3):838–848.

- Andr ́ee, B. P. J. (2020). _Theory and Application of Dynamic Spatial Time Series Models_ . Rozenberg Publishers and Tinbergen Institute, Amsterdam.

- Andr ́ee, B. P. J. (2021). Monthly food price estimates by product and market. In _WLD_ _~~2~~ 021 RTFP_ _~~v~~ 02_ _~~M~~_ , Version 2021-12-02. World Bank Microdata Library, Washington, DC.

> 30Results and adapted code are available from the author upon request.
<u>Table 1—Year of data collection and of FPE reform</u>

||||SDI Data<br>|||
|---|---|---|---|---|---|
||Collection<br>Year<br>(1)|Number<br>of Schools<br>Surveyed<br>(2)|Number of<br>Grade 4<br>Students<br>Assessed<br>(2)|Number of<br>Grade 4<br>Teachers<br>Assessed<br>(3)|Free Primary<br>Education<br>Reform Year<br>(4)|
|Kenya|2012|306|2,370|1,119|2003|
|Madagascar|2016|472|3,950|522|2003|
|Mozambique|2014|203|1,744|310|2004|
|Tanzania|2016|198|4,236|1,327|2002|
|Togo|2013|396|1,886|195|2008|
|Uganda|2013|400|3,354|720|1997|
|Total||1,975|17,540|4,193||

Note: Number of teachers includes only those who were assessed and for whom the start date is known.

22
- Cybert, R.M., J.G. March. 1963. _A Behavioural Theory of the Firm._ Prentice Hall, Englewood Cliffs, NJ.

- Dai, R., Feng, H., Hu, J., Jin, Q., Li, H., Wang, R., Wang, R., Xu, L., Zhang, X. (2021a). The impact of COVID-19 on small and medium-sized enterprises (SMEs): Evidence from two-wave phone surveys in China. _China Economic Review,_ 67, 1-16.

- Dai, R., Mookherjee, D., Quan, Y., & Zhang, X. (2021b). Industrial clusters, networks and resilience to the Covid-19 shock in China. _Journal of Economic Behavior & Organization_ , 183, 433-455.

- Demers, E., Hendrikse, J., Joos, P., & Lev, B. (2020). ESG didn’t immunize stocks against the COVID-19 market crash. _Available at SSRN 3675920_ .

- <mark>Dierickx, I., K. Cool. 1989. Asset stock accumulation and sustainability of competitive advantage.</mark> _<mark>Management Sci.</mark>_ <mark>35, 1504-1511.</mark>

- <mark>Ding, W., Levine, R., Lin, C., & Xie, W. (2021). Corporate immunity to the COVID-19 pandemic.</mark> _<mark>Journal of Financial Economics</mark>_ <mark>.</mark>

- Erdem, O. (2020). Freedom and stock market performance during COVID-19 outbreak. _Finance Research Letters_ . Forthcoming.

- Fahlenbrach, R., Rageth, K., & Stulz, R. M. (2020). How valuable is financial flexibility when revenue stops? Evidence from the COVID-19 crisis (No. w27106). _National Bureau of Economic Research_