rafmacalaba/data-use-mentions-extended huggingface dataset:
M. (2003), “Targeted Transfers in Poor Countries: Revisiting the Tradeoffs and Policy Options”, World Bank Policy Research Working Paper, No. 3048 Sahn, D., D. Stifel and S. Younger (2003), “Exploring Alternative Measures of Welfare in the Absence of Expenditure Data”, _Review of Income and Wealth_ , 49(4), pp463-489 Ugaz, C. and C. Waddams Price, ed. (2003), **Utility Privatization and Regulation: A Fair Deal for Consumers?,** Edward Elgar, Northampton, MAS, USA Van de Walle, D. And K. Nead (1995), **Public Spending and the Poor: Theory an Evidence** , John Hopkins Press, Baltimore Van de Walle, D. (1998a), "Infrastructure and Poverty in Viet Nam." In Dollar, Glewwe and Litvack, eds., **Household Welfare and Vietnam's Transition to a Market Economy** , World Bank, Washington, DC.

Van de Walle, D. (1998b), "Targeting Revisited," _World Bank Research Observer_ ,13(2): 23148.

Van de Walle, D. (2002), “Choosing Rural Road Investments to Reduce Poverty,” _World Development,_ 30(4): 575-589 Van de Walle, D. (2003), “Are Returns to Investment Lower for the Poor? Human and Physical Capital Interactions in Rural Viet Nam”, _Review of Development Economics, 7_ (4): 63653, 2003.

Wodon, Q. , M.I. Ajwad and C. Siaens (2004), “ Lifeline or Means-Testing? Electric Utility Subsidies in Honduras”, in P. Brook and T. Irwin, editors Wodon, Q. (1997), “Targeting the Poor Using ROC Curves”, **World Development** , 25, pp 2083-92
of Vietnam. Minot _et al._ (2003) construct a poverty map using a 1998 VLSS and a 33% sample of the 1999 Population and Housing Census. Nguyen (2009) applies the 2002 VHLSS to the 33% sample of 1999 Population and Housing Census to produce a poverty map for 2002. Nguyen et al. (2009) further update the rural poverty map for 2006 using the 2006 VHLSS and the 2006 Rural Agriculture and Fishery Census.

3
at work means that even if the worker is in an occupation that is exposed to GenAI augmentation, such potential productivity gains are unlikely to realize given the lack of access to digital infrastructure. We first implement this exercise for the four countries in the LAC region (Chile, Ecuador, Mexico and Peru) and two developed economies (Slovenia and New Zealand) included in the PIAAC dataset.<sup>18</sup> 17 For the main results presented in the paper, we use variable “g_q04”, which contains the response to the following question: “Do you use a computer in your job?/Did you use a computer in your last job?”. We also do robustness checks by creating a binary variable equal to 1 when the worker uses both a computer and internet at work, by using the variables “g_q05a”, “g_q05c”, “g_q05d” and “g_q05h”, which contain information about the frequency (i.e. “never”, “less than once a month”, “less than once a week but at least once a month”, “at least once a week but not every day”, or “every day”) of internet use for mail, work related information, conduct transactions and participate in video calls, respectively. More specifically, when the worker responds that he or she “never” uses the internet for any of those four reasons at work, we consider that the worker does not use internet at work. If the worker responds that he or she uses the internet at work for any of those purposes with any frequency other than “never”, then we consider that the worker uses internet at work. Simultaneous use of computer and internet presents a more restrictive condition, which results in greater digital gaps. These calculations at the country level are shown in Figure A5. More detailed statistics are available upon request.

18 While PIAAC includes several developed economies, we did not choose them for this initial step of
observations from item-district cells that have insufficient representation to construct a price schedule (fewer than 15 observations). This reduces the sample size by roughly 1%.

A further cleaning step was required to standardize units. Respondents reported many quantities in kilograms and liters, but others in bunches, heaps, tins, ladles, buckets or bundles. For some items these units were measured during the market price survey. For other commonly reported units, the team went back specifically to measure the kg or liter conversion. We use the median, district-level conversion rates to convert local units into kilograms or liters.<sup>7</sup> The final data set contains details for 48,501 purchase transactions. Descriptive details for the final set of 19 study items are reported in Table 1. Maize and cooking bananas— staple carbohydrates—are the items purchased in the largest kilogram quantities. Comparing transaction quantity to total 2-week consumption in Table 1, it is clear that households tend to buy items multiple times over two weeks. Table 2 provides more details on this and other patterns in the diary data. The total number of observed transactions ranges from 688 (maize) to 5319 (cooking oil). The average item was purchased by just under half of the sample (733 households), and was purchased multiple times by just over a third of the sample (505 households). Some items, such as cooking oil, kerosene, sugar, dried sardines, and onions, were purchased more than once by a majority of households. Among the households that purchase each item, the highest average expenditure is on maize at 7,354 TZS/household, and the lowest is on matches at 187 TZS/household. The average number of purchases per item is 3.6.<sup>8</sup> The SHWALITA team also conducted a market price survey in each village, in conjunction with the household survey. Markets are relatively dense in Tanzania: 97% of study households live within 10 kilometers of
severe allergic responses in exposed populations (Zahm, Ward and Blair, 1997).<sup>1</sup> Many pesticides used in Bangladesh are banned or restricted under international agreements (NOVIB, 1993; SUNS, 1998; SOS-arsenic.net, 2004). In addition, several studies have shown that inadequate product labeling and farmers’ lack of information have led to widespread overuse or misuse of dangerous pesticides. Substantial anecdotal evidence suggests that pesticide poisonings and ecological damage have become common in Bangladesh (Ramaswamy, 1992)

# **2.2  IPM in Bangladesh**

Bangladesh’s IPM activities began with rice in 1981, and the FAO played a strong catalytic role with government officials and the donor community. The program provided capacity-building for the Department of Agricultural Extension (DAE), introduced Farmer Field Schools, and trained representatives of local NGO’s. Subsequently, the government and NGO’s initiated several IPM projects for rice and vegetables with donor funds.<sup>2</sup> At present, the Plant Protection Wing of the DAE is responsible for the implementation of IPM activities (FAO, 2001).

# **3.  Survey Data**

The research reported in this paper is based on a large survey of Bangladeshi farmers, carried out by the World Bank in the summer of 2003. We used structured questionnaires to collect information on conventional and IPM farming techniques, pesticide use and practices, applicator precautions and damage-averting behavior, health effects and environmental impacts.<sup>3</sup> To provide greater depth, we also interviewed 139 randomly-selected rice farmers who currently use IPM.

> 1 See Appendix I for details.  The FAO study has found particularly high shares for carbamates and organophosphates, which pose the health hazards noted above.

- 2 To date, major IPM programs in Bangladesh have included the DAE-UNDP/FAO IPM Project (BGD/95/003); DAE-DANIDA Strengthening Plant Protection Services (SPPS) Project; Command Area Development Project (CAD);
for “risk-free” UK and US government bonds (marked in blue) using the full 200-year series for both countries (the average yearly real return is 3.4% for US bonds and 4.1% for UK bonds, with standard deviations of 12% and 10%, respectively).

Figure 9 focuses on country credit histories and plots the average yearly real ex-post bond returns on the vertical axis against the total number of external sovereign default events (on private external debt) since 1815 on the horizontal axis, using the updated Reinhart and Rogoff (2009) default data. Similar to Figure 7, we find that bond returns increase in the riskiness of a country, as measured by the number of past defaults. In Appendix B7, we add to these results with a capital asset pricing model (CAPM) analysis. Serial defaulters have higher betas, as bond returns for these countries are more sensitive to US/UK stock market conditions.

Figure 9: The role of credit history: Returns and default frequency, by country, 1815-2016 <!-- Start of picture text --> CIV<br>15 ECU<br>CHE PAK<br>EST<br>NGA<br>IRQ UKR<br>FIN MEX<br>10 DEU POL SRB CRI<br>VEN<br>FRA CZK<br>PHL<br>LBN JPNITA PAN ZWEMARHTICUB PRTGRC COLSLVBRA URYPRYNICDOMGTM CHLARG<br>5 NLDSWEMYSDNKBEL VNMHRV HUN BGR RUSIDNHNDAUT EGY ESP PER TUR<br>NORIRL THA ROUZAF<br>US bondsNZLAUS<br>CAN<br>UK bonds CHN BOL<br>t-statistic:1.87<br>p-value:0.07<br>0<br>0 1 2 3 4 5 6 7 8 9 10 11<br>Number of external defaults<br>Serial defaulters All others<br>Mean annual real return in %<br><!-- End of picture text --> _Notes:_ This figure
econometrically, one can resort to model _calibration_ . This process solves relevant equations for values of the parameters that are consistent with base year data. For the parameters of the Cobb-Douglas production functions we have the following expressions: The parameters of the Armington function are given by the following: Based on the above expression and the base data presented in SAM (table 2.1) the calibration process led to the results presented in table 2.2. This information reveals that the export sector is labor intensive relative to the domestic sector. The latter is more capital intensive than the former. Thus this stylized model may be thought of as representing a developing country exporting a primary agricultural commodity produced 10
# **3.2.1. Treatment 0: 14-day diary**

The design and implementation of the 14-day diary (henceforth, diary or T0) in this study followed exactly the methodology in the 2017/18 HBS (see Ministry of Finance, NBS, and World Bank 2020). The methodology required administering the 14-day diary as part of a multi-topic questionnaire with multiple visits to the household. During the first visit, the enumerator introduced the food consumption diary to the household, gave them training on how to complete it, and left it with the household/individuals to complete on a daily basis. Daily expenditure information on food items purchased, the quantity purchased and corresponding unit (including non-standard units), value of the quantity purchased, source and destination of the quantity purchased were recorded. In addition, households were asked to record separately the actual daily food consumption, as well as FAFH. The enumerator revisited the household at different times during the 14 days to supervise and transfer the information from the daily record book into the main CAPI questionnaire. At the end of the 14 days, the enumerator revisited the household (on the 15<sup>th</sup> day) to close the diary with them. Once the information was transferred into the main questionnaire, COICOP codes were assigned to each of the food items that the household indicated to have consumed/purchased. The other modules in the main questionnaire were administered anytime between the first and the last visit to the household. Thus, the number of food items, the number and phrasing of questions, and methodology of diary implementation in this study (including frequency of enumerators visits to the household) were exactly the same as those used in the 2017/18 HBS. A separate FAFH module was also implemented at the individual level for this group. Food consumption aggregates were then generated using the quantity consumed and price information collected in this module, while adjusting for temporal (within survey months) 8
Policy Research Working Paper 7989

# **Abstract**

This paper describes the design of a multi-stage stratified sample for the Bangladesh Household Income and Expenditure Survey 2016/17. This survey instrument will be used by the Government of Bangladesh to estimate reliable poverty and welfare statistics at three different levels: (i) annual estimates at the district level, (ii) quarterly estimates at the national level, and (iii) annual estimates at the division level for urban and rural areas. The sample for this survey was designed to achieve these three objectives. The paper explains how the three objectives are prioritized and how inconsistencies in achieving more than one objective can be reconciled. Further, the paper modifies the standard formulas to estimate the optimal sample size and the allocation of the sample across strata by explicitly taking into consideration the effect of clustering in the sample.

This paper is a product of the Poverty and Equity Global Practice Group. 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 authors may be contacted at nyoshida@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 the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
values defined by the confidence intervals. These non-trivial confidence intervals reflect the reality that measuring governance is difficult, and that different data sources will come to different conclusions about countries’ relative positions on the aspects of governance that they measure. We note that this problem is not a consequence of our use of perceptions data to construct the WGI. Even if we were to rely on a set of objective indicators to construct the aggregate WGI measures, these data sources would also differ in their assessments of governance across countries, again leading to margins of error. Indeed, most indicators of governance or the investment climate, or economic indicators more broadly, are subject to measurement error. A key feature of the WGI is that we transparently disclose this measurement error 18
inorganic fertilizer use is profitable and how that varies across space, the profitability of subsidized fertilizer vs. an equivalent cash transfer to beneficiaries, and the profitability of government recommend fertilizer application rates vs the actual application rates of smallholders in our sample. The study adds to the fertilizer profitability literature by controlling for plot-level unobserved heterogeneity, in addition to considering various maize prices that farmers face across time and space, and by relating the estimates to the geographical targeting of large-scale fertilizer subsidy programs.

The main results of our study suggest that when we assume farmers face positive transaction costs when using fertilizer, the input is on average not profitable at commercial price of fertilizer when maize is valued at either the farm gate price or lean season market price. The study also finds that, in order to make fertilizer more profitable at prevailing market conditions, the current average nitrogen use efficiency of 11.89kg would have to increase by at least 137.17% and by 50.46% if maize output is valued at farm gate price and lean season market price, respectively. Though these NUE estimates are lower than those used in other recent studies, such as Dorward et al. (2008), Arndt et al. (2015) and Harou et al. (2017) our use of recently collected nationally representative panel data on smallholders using plot-level FE could provide internally and externally valid estimates that are useful for policy analysis.

Our results are consistent with Arndt et al. in that our NUE of 11.89 suggests that FISP does not break-even. This is why we find that at all rates of subsidy, unless farmers are able to store their produce and sell in the lean season, the average farmer is MK 66.16 (US$ 0.18) per kg of subsidized nitrogen better off with the cash equivalent of the subsidy than participating in the subsidy program if maize is valued at the farm gate. Finally, the study finds that, compared to the current rate of nitrogen application, the government recommended rate is
in the Center-South model, which are positively correlated (Table 3). In the new models, household size and not owning a house have a negative association with consumption; employment variable, in contrast, has a positive association. Surprisingly, not having access to public network appear to be a positive predictor of a household’s measure of welfare (Table 4). These pseudo coefficients are applied on the appropriate beneficiary registration database to understand what proportion of the existing humanitarian caseload would be eligible for the government’s cash transfer program.

_Table 3. Pseudo-PMT for the 2016 VM model_

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|**Kurdistan**|**North**|**Center- South**|

9
# **_B. Surveys_**

The data set includes a face-to-face survey, two mobile phone surveys (baseline and end line), and call-in data collected in 2016. While the face-to-face baseline survey was conducted during March and the first two weeks of April, the baseline mobile phone survey was implemented in the last two weeks of April. The call-in data was recorded throughout the intervention period (May), and the end line mobile phone survey was carried out in June.

Prior to the baseline face-to-face survey, households were randomly assigned to a long survey or short survey.<sup>20,21</sup> The long survey collects comprehensive household and individual-level information, while the short survey includes only very basic listing information. Short survey participants were invited by the enumerator at the end of the face-to-face interview to participate in the second part of the baseline survey, which would be conducted shortly after the face-to-face interview via mobile phone using interactive voice response (IVR) technology.

Mobile phone surveys are required to be concise in order to achieve satisfactory response rates, usually limiting the number of questions to roughly ten. The mobile phone survey is restricted to the outcome variables described below. Enumerators briefly trained households in the use of the technology by conducting mock surveys and providing a written summary of instructions. Senegal is classified as a good candidate for phone surveys based on linguistic fractionalization and mobilephone penetration rate (Ben et al., 2015). The endline mobile phone survey consists of 15 variables and was administered to all households that had completed the (short) face-to-face baseline survey.

# **_C. Variable Description_**

The primary outcome of this experimental study is the increased adoption of solar lamps (Coville et al., 2015). Specifically, outcome indicators are binary indicators for the extensive and intensive margins of solar lamp adoption. For the extensive margin we use dummy variables for whether the household has ever owned a solar lamp. For the intensive margin, households were asked whether they currently own
# **Outcomes**

To analyze the impact of inflation on household livelihoods, our dependent variables are household level (and individual level) outcome indicators. The variables cover a range of household social and economic indicators, which can be calculated based on the panel data (waves 1, 2 and 4). Table 2 shows the sample for the analysis compared to the initial sample. The outcome variables are selected from the following five categories: poverty, education, labor, hunger, and perceptions of welfare (Table 3).

Table 2: Sample size

||Initial Sample Size|Sample Size for the Analysis|
|---|---|---|
|Wave 1|3550|423|
|Wave 2|1189|423|
|Wave 4|944|423|

Source: High Frequency South Sudan Survey Table 3: Outcomes variables

|**Variable**|**Description**|
|---|---|
|**Poverty **||
|Poor or non-poor|Whether the household is poor or not based on the $1.90 2011 PPP poverty line|
|Consumption|Householdconsumption expenditure in real terms|
|**Education**||
|School attendance|Whether children aged between 6-13 years and between 14-18 years are currently<br>attending school|
|**Labor**<sup>**4**</sup>||
|Labor force participation rate|The ratio of the active in the labor force to the total working age population (15-64 years)|
|Employment rate|A person is employed if he/she is of working age and has engaged in one form of<br>employment activity.<sup>5</sup><br>The employment rate is the number of persons in employment as a percentage of the<br>total labor force.|
|Unemployment rate|A person is unemployed if he/she is of
7 have narrowed over time (cf. Bredenkamp et al. 2014). One can document changes and differences in ‘financial protection’ in health; one can see, for example, whether the incidence of ‘catastrophic’ and ‘impoverishing’ health expenditures varies across countries (cf. Wagstaff and Eozenou 2014) or has changed over time in a specific country, before or after a reform, or relative to trends in neighboring countries. One can analyze (equality in) service coverage and financial protection simultaneously under the UHC umbrella (cf. Wagstaff et al. 2015; Wagstaff et al. 2016). More generally, the database is likely to be useful for analyzing any health system regarding how well it does in terms of delivering health services and improving health outcomes, but not compromising families’ ability to pay for goods and services other than health care.

This paper provides a brief history of the HEFPI database, gives an overview of the contents of the 2018 version of the database, and then gets into the detail of the construction of its two sides – the health equity side, and the financial protection side. It also provides illustrative uses of the data set.

# **A brief history of the HEFPI database**

The 2018 HEFPI database is, in effect, the fourth in a series of similar World Bank databases, all of which draw exclusively on data from household surveys – see Figure 1. The first two (Gwatkin et al. 2000; Gwatkin et al. 2007) showed gaps within and between countries on various indicators of service coverage and health outcomes in the MDG areas of maternal and child health (MCH), and communicable disease. The 2000 data set covered just 42 countries and drew data from just 42 surveys in the Demographic and Health Survey (DHS) family. More DHS surveys were added in 2007. The 2012 database (Bredenkamp et al. 2012b, c, d, e, a) also included data from UNICEF’s Multiple Indicator Cluster Survey (