rafmacalaba/data-use-mentions-extended huggingface dataset:
it was exchanged or not. This position was formalized in the 1993 SNA, which specified that household production of agricultural goods for own-use falls within the SNA production boundary, and hence, ought to be included in GDP – at least in countries where it is thought to be quantitatively important. Conversely, services for own use (such as cleaning, childcare, and meal preparation) have always been excluded from the SNA production boundary (and from GDP). This distinction between goods and services for own use has been extensively criticized, particularly (but not exclusively) by feminist economics scholars, who argue that national accounts statistics hide unpaid work performed mostly by women and do not recognize the productive value of these activities (Waring 1990; Benería 1999; Goldschmidt-Clermont and PagnossinAligisakis 1999; Young 2000; Goldschmidt-Clermont 2000; Anker 2011). Even though the topic was debated at two United Nations World Conferences (Swiebel 1999), the broad distinction between own-use goods (included in GDP) and own-use services (excluded from GDP) has been maintained in the 2008 5
where _osc_ is the number of self-employed people, and _esc_ the number of (wage) employed people.

The denominator sums total private sector employment (both wage and self-employed, formal and informal) across all 13 sectors and 47 counties. The updated sector-region-size probabilities _p_ ( _θa_ ) will again sum to one and hence capture a relative change in the number of firms rather than an absolute change.

Using simple employment shares to compute _p_ ( _θa_ ) relies on the assumption that the mapping of employees to firms is the same across all sectors and regions. However, empirically, manufacturing firms, for example, tend to be larger than businesses in the hospitality sector. Nairobi hosts larger firms than Mandera County in Kenya’s north. We therefore re-scale the number of employees by the average firm size in each sector-county-size cell _<u>x</u>_<sup>_~~f~~_</sup> _sc_<sup>_ormal,informal_</sup> . For small formal and informal firms, we rely on the KNBS (2016) survey to compute the average number of employees, while we use the administrative data for large formal sector firms.<sup>24</sup> For agriculture and non-market services, we estimate their _p_ ( _θa_ ) drawing only on formal private sector employment. Formal VAT-paying firms occupy a very specific niche in both cases (e.g. formal firms in these sectors are disproportionately export-oriented or the sector is dominated by non-profit entities, see discussion in Section 2.2) and informal employment takes vastly different forms (e.g. mainly reflects subsistence farming).

How does the probability _p_ ( _θ_ ) that a formal firm enters in a given sector-county-size cell shift to _p_ ( _θa_ ) once we account for informal firms? Figure C1 suggest that a 10 percentage point increase in formality corresponds with a 0.5 percentage point increase in _p_ ( _θ_ )- _p_ ( _θa_ ) (0.35 standard deviations).
existing EWS that have been developed in Ethiopia can be used to inform early action, and based on the continued collection of EWS data and continued improvements to the corresponding tools, there is scope for using EWS to inform an early and timely drought response.

The paper is structured as follows: section 2 will present the policy and institutional background for Disaster Risk Management (DRM) in Ethiopia. Section 3 will provide an overview of the existing drought response framework within Ethiopia. Section 4 presents existing early warning tools in Ethiopia, in particular, the LEAP and LIAS tools, as well as the GoE’s bottom-up early warning framework, hotspots assessments and the use of the Integrated Food Security Phase Classification. Section 5 investigates the timing of the availability of early warning information, as opposed to the HRD appeal process, and links this to the Ethiopian seasons. It discusses also the welfare benefits of an early drought response. Section 6 discusses and concludes.

# II. Policy and institutional background

Recognizing the need to systematically address drought risks and to protect Ethiopians from the loss of lives, livelihoods and income, the GoE has taken significant steps to systematically manage disaster risks. Efforts to manage drought risks date back to 1974, when the Relief and Rehabilitation Commission (RRC) was established in the aftermath of the severe 1973 drought. In 1995, the RRC was transformed into the Disaster Prevention and Preparedness Commission (DPPC). The strategic oversight of DRM in Ethiopia was vested in the National Disaster Prevention and Preparedness Committee (NDPPC), with the DPPC acting as NDPPC’s Secretariat to implement DRM policies and decisions. In 2004, DPPC was renamed the Disaster Prevention and Preparedness Agency (DPPA) and given narrower responsibilities to focus on emergency response, while a new Food Security Coordination Bureau (FSCB) was created to address issues of chronic food insecurity<sup>18</sup> .

In 2007, as part of a government-wide Business Process Re-engineering (BPR), the GoE transferred the responsibilities of the DPPA to the
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|Wish could have more respect for myself (Rosenberg)|1843|0.42|0.60|1435|0.40|0.57|-0.07|-0.13|0.00|
|---|---|---|---|---|---|---|---|---|---|
|Certainly feel useless at times (Rosenberg)|1843|1.33|1.10|1435|1.40|1.07|-0.03|-0.16|0.10|
|At times think I am no good at all (Rosenberg)|1843|1.32|1.09|1435|1.43|1.07|0.00|-0.12|0.12|
|Psychological well-being index|1843|0.00|1.00|1435|0.05|0.99|0.06|-0.06|0.17|
|Felt cheerful last 2 weeks (WHO)|1843|2.59|1.72|1435|2.64|1.64|0.05|-0.12|0.22|
|Felt calm & relaxed last 2 weeks (WHO)|1843|2.62|1.68|1435|2.68|1.59|0.06|-0.11|0.22|
|Felt active & vigorous last 2 weeks (WHO)|1843|2.57|1.64|1435|2.61|1.54|0.04|-0.13|0.21|
|Woke up fresh & rested last 2 weeks (WHO)|1843|2.58|1.62|1435|2.56|1.53|0
prediction error of the standard deviation, defined as SDt −SD�t (re-scaled to 10<sup>2</sup> ), where SDt is the realized standard deviation in the post-crisis regime.

Figure 4 depicts the distribution of SDprediction error. The median of the prediction error is - 0.424, which means that farmers predicted a larger price volatility than the realized level of price volatility. . There were, however, many farmers who experienced a larger price volatility than they expected. If the realized price is more volatile in the post-crisis regime than they expected, farmers should have a smaller incentive to increase investments in response to the food price spike and would increase pre-cautionary savings. The result in column (3) shows that SDprediction error has a negative sign but insignificant. Even in this specification, the results on anticipated and unanticipated shocks remain robust as we found in column (1) and (2).

This finding reinforces our arguments that not all farmers who anticipated a positive price shock invested in agricultural productive assets, but poorer farmers did.

# **6. Conclusion**

In this paper, we have examined farmers’ investment decisions during the food crisis period in Indonesia using recent household panel data. The empirical analysis showed the positive price impact on farmers’ investments responding to the food price increase experienced in the period 2007 - 2008. Unlike the negative welfare impacts of higher food prices for consumers studied in the previous literature, we found that the anticipated component of the price shock created a 24
of the national poverty lines in Portugal and Italy, and the upper bound is twice the median income in Luxembourg. That is, to be in the global middle class, one cannot be poor in the poorest rich countries, and cannot be rich in the richest country. We apply these bounds and estimate the share of the middle class as the headcount ratio for the upper bound minus the headcount ratio for the lower bound.

This middle class share variable combines measures of both equality and average income. To measure equality, we use the average Gini coefficient for each country reported (Milanovic, 2013). For average income, we use data on real GDP per capita from the Penn World Tables 9.1 (Feenstra, Inklaar, and Timmer, 2015). A difference between our approach to measure the global middle class and that of Kharas (2010) is that he uses average household consumption for average income, whereas we use GDP per capita. GDP includes expenditure in the investment sector (i.e. construction, machinery and equipment), thus increasing average income and making our estimates of the 15
in the economic analysis of inflation, which tends to be centered around the macroeconomic causes and consequences of fast price increases. Likewise, the varying incidence of inflation is frequently not captured in household welfare analysis that is carried out using “headline” rates of inflation to proxy for the changes in prices faced by the poor.

We estimate decile inflation rates for Turkey –a country battling high inflation in recent years– for the period 2011-2020 using consumer expenditure survey data and item-group specific price data from the official Turkish Statistical Institute. The results show that inflation is typically higher for vulnerable populations, especially when inflation is propelled by fast increases in food prices and housing costs, the most prevalent items in the consumption basket of a typical poor household. Differences in cumulative inflation between 2001 and 2020 show that the consumption bundle of the poorest decile has become 5 percentage points more expensive than the richest decile’s basket, on average a yearly gap of 0.23 percentage point. However, this estimate is most likely a lower bound of the real gap owing to the fact that lower-income households tend to pay higher prices within the same categories of goods –for instance, due to liquidity constraints that prevent them from buying larger quantities and taking advantage of bulk discounts, an issue that we do not investigate in this paper but that is documented in the literature (for instance, Kaplan and Schulhofer-Wohl 2017; Orhun and Palazzolo 2018).

Measuring the inflation realizations of vulnerable groups provides valuable information for policy for a number of reasons. First of all, monetary policy is often guided by macro models built around the welfare maximization of a representative household that is assumed to face average inflation. Therefore, the optimal monetary policy resulting from solving such optimization problem may result in larger welfare losses for households that have to deal with higher inflation. Moreover, also from a macro perspective, heterogeneity in inflation experience may anchor higher inflation expectations across some groups of the population, undermining policy efforts to curb the overall inflation rate (Johannsen 2014).

There
only includes labor incomes. In in the task content of a particular occupation, suggesting that the task content of occupations is not necessarily constant across countries.

5 O*Net provides a list of 41 work activities with importance indicator scores on a scale of 0-100 for each 6-digit SOC code. We weight up the indicators to our activities using data from the US BEA Industry and occupation matrix for 2020. We establish the composition of O*NET 6-digit SOC codes in each of our activities, mapping from 6-digit SOC to our 13 occupational groups and from BEA 2-digit SIC to our industry groups. Next, we test for significant differences in the averaged importance scores across different activity pairs, weighting with the number of workers in each detailed SOC occupation. For ease of computation the 41 indicators are reduced to 10 principal components first, capturing 83% of the variation. Wilks’ test result for the joint occupation-industry variable is (F-value = 4.92 and p-statistic = 0.00), for the occupation dimension (F= 46.98, p=0.00) and for industry dimension (F=0.54 and p=1.00).

6 ADB MRIOTs are not available for the years 2001-2006. The MRIO database vintage used in this study was accessed in October 2021.

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**Figure 7. Short-Term Correlates of Mobility, Ordered Logit Model with Random Effects, Marginal effects, RLMS 1994-2015**

<!-- Start of picture text --> Panel A: 1994-1998 Panel B: 1998-2004<br>10<br>5<br>0<br>-5<br>Panel C: 2004-2009 Panel D: 2009-2015<br>10<br>5<br>0<br>-5<br>To formal sector To full-time employmentUpward skills mobility To public sector To formal sector To full-time employmentUpward skills mobility To public sector<br>No transition No transition No transition No transition No transition No transition No transition No transition<br>Mobility(%)<br>Mobility(%)<br><!-- End of picture text -->

**Note:** Orange/green lines are related to 95% confidence intervals. Data on formal sector are available since 1998 and data on public sector are available since 2004. The estimation sample is restricted to individuals who are 18 years old and older. The dependent variable is income mobility between year _t-1_ and year _t_ . The terciles are defined using the cross-sectional sample for each year. Incomes are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. All control variables are measured in the reference year _t-1_ except for the occupation transition variables, which are the changes between year _t-1_ and year _t_ .

48
the survey experiment focused on the two key dimensions discussed above: (i) the level of detail of the questionnaire, more specifically the screening questions to establish employment status, and (ii) the type of respondent, namely self versus proxy response. Households were randomly selected and allocated to one of the four survey assignments based on these two dimensions. All household members aged 10 or above were eligible to respond to the individual‐level module on employment, and we consider all labor force participants to estimate the returns to education.

> 14 This is the approach followed by standard surveys like for instance Household Budget Surveys (HBS), Household Income/Consumption Expenditure Surveys (HICES) and Core Welfare Indicator Questionnaires (CWIQ) among others.

> 15 See Grosh, and Glewwe (2000)

> 16 Interviewing only self‐respondents may require many re‐visits, which can be costly.   Response by proxy rather than individuals themselves reflects the common practice to interview an informed household member (often the household head or spouse), rather than each individual him or herself. In practice proxy respondents are often used when individuals are away from the household or otherwise unavailable in the time allotted in an enumeration area to conduct interviews.

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**Figure 2.** Percentage of households at the bottom 40 of the expenditure distribution, by gender of the household head.

_Note_ : Own calculations based on ProGres and JD-HV database. The graph shows the percentage of households below the percentile 40<sup>th</sup> by gender of the PA.

Besides being more prone to poverty, households with female Pas are more likely than male Pas to be in lower deciles of the expenditure distribution. In Figure 3, Panels A and B show the percentage of households below the 40<sup>th</sup> percentile (deciles 1-4). Female-PA households in both waves are slightly over-represented in the bottom decile of the expenditure distribution: 1 in 4 poor female-PA households are in the first decile, the equivalent figure is 1 in 5 for their male counterparts. Panel B of figure 3 shows that when adjustment is made for economies of scale, the gap of how intensely female-PA households experience poverty is about the same.

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# **Data Triangulation Strategies to Design a Representative Household Survey of Hosts and Rohingya Displaced in Cox’s Bazar, Bangladesh**<sup>**1**</sup>

Joaquin Endara Maria Eugenia Genoni Afsana I. Khan Walker Kosmidou-Bradley Juan Muñoz Nethra Palaniswamy Tara Vishwanath Keywords: Forced displacement, Rohingya, sampling methods, host populations, surveys JEL codes <mark>: C83, I3, F22, R23</mark>

> 1  Authors are members of the World Bank Poverty and Equity Global Practice. This analysis was undertaken for the design of the Cox’s Bazar Panel Survey (CBPS). The CBPS is the result of a partnership between the Yale Macmillan Center Program on Refugees, Forced Displacement, and Humanitarian Responses (Yale Macmillan PRFDHR), the Gender & Adolescence: Global Evidence (GAGE) program, the Poverty and Equity Global Practice of the World Bank. Funding provided by the State and Peacebuilding Fund (SPF). The SPF is a global fund to finance critical development operations and analysis in situations of fragility, conflict, and violence. The SPF is kindly supported by: Australia, Denmark, Germany, The Netherlands, Norway, Sweden, Switzerland, The United Kingdom, as well as IBRD.
percent of the fiscal impoverished are members of households identified as poor according to the official definition. This result is indicative of the additional efforts required to improve the targeting and expand the coverage of direct transfers among the poor population, in particular because direct transfers have a significant large effect in reducing inequality and poverty.

Finally, when put into a regional perspective, the redistributive effect of direct transfers (i.e., the decline in inequality from pre-fiscal income to post-transfers income) is particularly larger in Chile than in other Latin American countries with a comparable fiscal incidence analysis. A remarkably result is that with 1.6 percent of the GDP on direct transfers, the redistributive gains in Chile are as much as 2.7 times larger than in Bolivia and virtually the same as in Brazil, where direct transfers account for 2 and 5.5 percent of the GDP, respectively. In terms of the effect on poverty, as measured by the $4/day poverty line, direct transfers in Chile reduce the incidence by 41.2 percent with respect to prefiscal income, placing the country as a top performer in the region.

As part of its efforts to address persistently high levels of income inequality, the Government of Chile enacted a comprehensive tax reform in 2014 aimed at generating additional tax revenues (amounting to around 3 percent of the GDP) to finance social spending, specially on education; improving neutrality and equity of the tax system; improving the efficiency of tax incentives on savings and investment; and reducing tax evasion and avoidance (Arenas de Mesa, 2016). Even though the effect of such reform is not quantified in this paper, further spending on education could potentially contribute to income inequality decline in the medium and long-term. Moreover, an ex ante evaluation of the reform using the 2013 fiscal records suggested that the tax reform would likely increase the effective tax burden for the top 1 percent of the income distribution by 6 percentage points (equivalent to an increase from 2.4 to 3.5 percent
decreasing with distance to the frontier, i.e. especially significant for industries identified as neck‐and‐ neck.

As in our previous extensions and robustness checks, we prefer to work with the Ackerberg specification in our regressions and so our results show the coefficient outcomes accordingly. The results of interacting our regulatory variables with the technology gap are presented in Table 7. In this table it becomes clear that the separate services barriers overall in the left‐hand panel has the expected negative sign and that the interaction term using this regulatory variable also have a negative and significant outcome in column (1). Splitting up the variable again, it shows that in columns (5) and (6) entry barriers obtain a negative and significant result while conduct regulations obtain a positive and significant result although weakly. This indicates that entry barriers are particularly hurtful for industries which are classified as unleveled, but that conduct barriers have a particularly negative impact for industries that are categorized as neck‐and‐neck.

We have performed alternative specifications to come up with a robust result. For instance, the results remain stable when using the EU‐wide input coefficient results as shown in the right‐hand panel. We also obtain similar results when including a dummy variable that distinguishes between the two types of industries (output omitted). Moreover, recent papers such as McMorrow _et al_ . (2010) and Bourlès _et al_ . (2013) have used alternative technology gaps based on non‐firm‐level data. We follow Bourlès _et al_ . (2013) so that our alternative specification of technology gap is the simple difference of ln(TFP _F_ ) and ln(TFP _i_ ) and then averaged by sector.<sup>26</sup> The results support the idea that entry barriers are particularly important for unleveled sectors whereas conduct regulations are more important for neck‐and‐neck sectors. Additionally, the last two papers use TFP growth rather than levels which we are also able to compute as the log difference
available but require a stricter two-step application process in which the organization of a researcher first has to be recognized as a research entity – a university, research institution or research department in a public administration, bank, statistical institute etc., after which a researcher can submit an application to receive the full microdata set.

In the MENA region there is less of a tradition of making microdata available and few countries seem to provide public access to (anonymized) microdata. For example, Atamov et al. (2020) report that in 2019 only 7 of the 20 countries in the region provided public or licensed access to household budget surveys which provide the source data on the basis of which the World Bank calculates its estimates of poverty (Table 1). To help with the advocacy of accessibility to microdata in MENA, it is important to have a more complete understanding of the state of microdata access, beyond the availability of household budget surveys. We do so in the remainder of this paper.

**Table 1. Status of public and WB access to household budget surveys in MENA as of August 2019**

**Public or licensed access** Djibouti Egypt, Arab Rep. Iran, Islamic Rep. Iraq Tunisia West Bank and Gaza Yemen, Rep. Source: Atamanov et al. 2020

5