rafmacalaba/data-use-mentions-extended huggingface dataset:
consumption is predicted at the level of each household in the population census, based on a consumption model estimated in the household survey. However, because predicted household-level per capita consumption in the census is a function not only of the parameter estimates from the consumption model estimated in the survey, but also of the precision of these estimates and of those parameters describing the disturbance terms in our consumption model, we do not produce just one predicted consumption level per household in the census. Rather, _r_ predicted expenditures are produced for each household (in our three countries, we have carried out one hundred predictions). For each respective _r_ , parameter estimates are drawn from a multivariate normal distribution that respects the variance-covariance matrices estimated in the survey-based consumption and heteroskedasticity regressions. In addition, disturbance terms at the cluster and household level are drawn from their respective parametric or semi-parametric distributions. These draws are then applied to the census-level regressors and per-capita consumption is predicted. For the next _r_ , a new set of parameters and disturbances are drawn and a new per-capita consumption measure is predicted. The resulting “dump-file” of _r_ predicted expenditures for every single household in the population census is the key database underpinning “poverty maps” and the policy-simulation exercise explored here.<sup>13</sup>

# _Simulating the impact of uniform targeting_

Our baseline, benchmark, policy simulation is calculated in the following way. Budget _S_ is divided by total population _N_ . The resulting transfer _a_ is added to each predicted expenditure

> fraction of the budget uniformly to each household in the community.  Note that this latter scheme ensures that all communities receive at least something.

> 13 The poverty map estimate of poverty in community, province or region _c_ is produced from this dump file in the following manner: for every replication _r_ , poverty is estimated over all households in _c_ (after weighting by household size).  The average of all poverty

17
# Introduction

Open a newspaper and chances are you will find some statistic referring to how the world is fairing: “Global growth is projected to recover”, “the number of refugees worldwide is set to increase for the third straight year”, “global CO2 emissions are reaching an all-time high.” The demand for global statistics is perhaps best embodied in the Sustainable Development Goals (SDGs), whose 231 indicators for the most part can be and are aggregated to the global level.

In reality, there is rarely complete global data behind such statistics. Due to lack of resources, capacity, and political will, some countries do not produce information on the indicators of interest (Dang et al. 2021; Serajuddin et al. 2015). When creating global statistics, estimates for these countries are either imputed or simply ignored. This inevitably creates a trade-off between the _availability_ of global statistics, and the _accuracy_ of these statistics. If global statistics are only published when data are universally or nearuniversally available, there will be many important topics that cannot be illuminated. If global statistics are published even when the data coverage is weak, the accuracy of the statistics may be doubtful in the sense that they are likely to deviate from the figure had all data been available.

In this paper, we quantify this trade-off between data accuracy and data availability using the World Bank’s World Development Indicators. We select 165 indicators spanning a wide range of topics where, for a given year, data are available for at least 99% of the world’s population. We randomly remove data from these indicators and calculate the expected difference in the global statistic as a function of the share of the world’s population without data. We show that if the fraction of the world’s population on which one lacks data is _x_ , then one should expect to be 0.37* _x_ standard deviations away from the true mean, and as much as _x_ standard deviations from the mean at times. Here the standard
nutritional requirements - a minimum caloric amount - and then complemented with the cost to satisfy non-food essentials (clothing, shelter, private expenditure on education and health, and so on) obtained from households whose total (food) consumption equals the cost of obtaining the nutritional requirements. Poverty lines are typically derived using data from consumption surveys and as such reflect the actual consumption and revealed preferences of households. Poverty lines are thus context-specific, reflecting the consumption needs for achieving the minimum standard in a given society (Ravallion et al. 2008). By implication, each country sets its own national poverty line.

Global poverty measurement does not use national poverty lines, but uses the International Poverty Line (IPL), which itself is anchored to the national poverty lines of the world’s poorest countries. This first international poverty line, based on research by Ravallion, Datt, and van de Walle (1991), was set a PPP$ 1.02 per day per person and became known as the dollar-a-day poverty line.

Since the early 1990s the IPL has been updated with some regularity. The latest update was produced in <mark>defned as the median of the</mark> 2022 following the release of the 2017 PPPs. In this iteration, the IPL was <mark>natonal poverty lines of 28 of the world’s poorest countries a</mark> nd set at PPP$ 2.15, with SDG 1 using it as a benchmark for poverty eradication (Joliffe et al. 2022). With the 2017 PPP update came the recognition that the IPL may be too low to act as an acceptable minimum standard of living for middle-income countries, with subsequent global poverty lines for lower-middle, upper-middle and even high-income countries published thereafter.

In the remainder of this paper, global poverty lines are used to explore self-reliance, with refugees deemed self-reliant when they earn an income greater than the applicable global poverty line. e . ss In the equation above, ii = 1 if yyii > zz , ssee
disasters to income, expenditure, malnutrition, health and education, often using (longitudinal) survey data and econometrics (Noy, 2016; Noy and DuPont, 2018). However, studies modeling the welfare implications _ex-ante_ , which would allow them to be embedded in traditional risk assessments, are scarce. As part of the World Bank’s _Unbreakable_ report, Hallegatte et al. (2016) (henceforth H2016) developed a modeling framework to evaluate the impacts of disasters to households’ welfare on a national scale, which over the years has been applied to more detailed case studies in the Philippines (Walsh and Hallegatte, 2019), the San Francisco Bay Area (Markhvida et al., 2020) and Bangladesh (Verschuur et al., 2020). Apart from quantifying the welfare impacts, showing that every 1 USD dollar in asset loss translates into a welfare loss equivalent to 1.6 USD in consumption, the model allows evaluating the wider benefits of both hard (e.g., increased protection) and soft (e.g., cash transfer, access to finance) adaptation options, helping to better target portfolios of interventions to those that benefit most from them in welfare terms.

However, several limitations are inherent to the H2016 modeling framework. First, the use of national survey data, with limited representativeness on a local or regional scale, restricts a more granular analysis of hotspots of welfare impacts within countries. Second, the limited spatial representation of the model prevents us from investigating the drivers of disproportionate welfare impacts. For example, this could be driven by poorer households being relatively more exposed to flooding (e.g., exposure bias) or being dependent on a more vulnerable asset base. Moreover, spatially quantifying the exposure of vulnerable households 2
Importantly, surveying a segment of the population that operates on the border of legality carries challenges with sensitivities. Non-response is not uncommon. To ensure the participation of respondents, it is important to go beyond convincing them that their responses will be anonymized. In particular, it is important to provide assurances that the information will not be used for prosecution. Questions should be formulated in ways that are the least invasive to respondents. Given these challenges of implementation, the team that implements the survey must be thoroughly trained to administer the questionnaire in the form of a conversation. This not only increases the participation of informal businesses in the survey, but also avoids respondent reticence. The need to approximate a conversation during the survey interview further emphasizes the importance of simplicity throughout the questionnaire.

# **2.3 Questionnaire Composition**

The Informal Sector Enterprise Survey questionnaire was developed following the principles outlined above. It covers a range of socio-economic topics that not only provide a granular picture of informal businesses and their owners, but also discern salient patterns and features of a typical informal business. While the exact set of questions asked during interviews changes across countries, a core set of questions has been consistently asked, enabling crosscountry analysis. In particular, informal businesses are asked about their origin—who started it, the source of initial funding, with questions probing whether or not it was out of the necessity for additional income. A sizeable part of the questionnaire is dedicated to exploring the background of the owners, age, gender, education, household environment, their experience with formal jobs, whether they immigrated from a different region/country, and other aspects of their life that provide information about owners' resources and opportunities as they relate to the informal business.

During the interview, informal businesses are asked questions about their operations in general, along with detailed questions about sales and employment (separating paid and unpaid workforce), collecting information for regular, the slowest, and the busiest months to account for the extent of seasonality that the
new Takaful programme perform much better in terms of progressivity than indirect taxes and subsidies, respectively. However, because direct taxes and transfers represent relatively small contributions to total revenues and expenditures, they have a limited impact on poverty and inequality reduction. For example, revenue from personal income taxes are about 9 percent of revenue from GST. Almost three times as much is spent on regressive electricity and water subsidies than in progressive direct transfers, and even within direct transfers, 75 percent more is spent on the bread subsidy cash compensation which is much less targeted to poorer households than NAF and Takaful. Inequality only falls 1.7 points after direct taxes and transfers and only a further 0.9 point once indirect taxes and transfers are included. As a consequence, the fiscal system falls short of its potential equalising and poverty reducing impact.

The COVID-19 crisis is expected to pose a significant risk to the near-term growth prospect and to poverty and inequality (Refaqat _et al_ . 2019). In this context, the need for fiscal reforms to raise additional revenues without affecting the poor and vulnerable is more pressing. We discuss potential reform scenarios that could help achieve such objectives. These include the expansion of direct cash transfers through Takaful (a policy being currently implemented), the elimination of the bread compensation scheme, a reform of the electricity residential tariff system and the elimination of the exceptions under the GST. Taken together, we estimate that the fiscal system could become more progressive while generating significant fiscal savings.

We begin by presenting the main fiscal instruments and their budgetary allocations. Section 3 describes the methodology by which we allocate fiscal instruments to household data. Section 4 presents the distributional assessment of the current fiscal system in Jordan; Section 5 puts the results in international context; section 6 presents some potential reform scenarios; section 7 concludes.

# 2. Fiscal Instruments

This section provides an overview of Jordan’s taxes and expenditures in 2018. Although 2019 data are available, the household survey used to determine which households pay different taxes
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- McKenzie
- _The Demographic and Health Surveys (DHS)_ : these surveys cover seven out of the eight countries in the region; they are nationally representative and provide data on women and their children’s health and other outcomes. The data collected in different countries is comparable because the surveys implement near-identical questionnaires across countries. Three core questionnaires are used to collect data by the DHS: a household questionnaire, a women’s questionnaire, and a men’s questionnaire. In this study, we mostly focus on the women’s questionnaire.

- _World Value Survey (WVS)_ : WVS is a cross-national opinion survey that monitors cultural values, attitudes, and beliefs using nationally representative surveys. It uses a rigorous high-quality research design to collect data using a standardized questionnaire in almost 100 countries. The WVS measures, among others, support for gender equality, attitudes toward work and family, and subjective wellbeing, and uses agree-disagree Likert scales to measure socio-cultural and political attitudes from respondents.

For the DHS we focus on six South Asian countries —Afghanistan, Bangladesh, India, Maldives, Nepal and Pakistan. For WVS, we focus on three South Asian countries —Bangladesh, India and Pakistan—for which data are available. The DHS and WVS data have been collected in several waves roughly over a period of 20 years, from 1990 to 2018. The coverage however, varies by country (see Table A2). For instance, while DHS data is available for Bangladesh in eight waves, there is only one wave of data, 2015, available for Afghanistan. The DHS collects data from women aged 15-49 years. The age range in WVS is wider, however, so we restrict our sample to 15-49 for comparability with DHS. The length and breadth of coverage of the DHS and WVS data across countries in South Asia allow us to examine changes in key gender-related outcomes and social attitudes. We trace women born over half a century, from 1940 to 2000.

Table A2: Data availability for DHS and WVS surveys
<br>.433|<br>.407|
|%: Tax|.279|.192|<br>.219|<br>.313|<br>.148|
|%: Quantity|.260|.327|<br>.312|<br>.227|<br>.370|
|The share of PC-participants posting bond|.282|.019|<br>.172|<br>.280|<br>.648|
|The average bond for bond-posting firms<br>(in 1,000 yuan)|32.4<br>I|13.0|<br>34.7|<br>36.1|<br>22.2|

Note. Based on the authors' calculation from _A Survey of Chinese SOEs: 1980-89._ In a few cases (16 percent of the companies) the manager posted a bond that was forfeited if he failed to achieve the contract's goals. As with bidding, posting a bond was more likely, the more local the government authority. The amounts are non-trivial, averaging about 32,400 yuan, several times a CEO's annual wages. SOE managers nder 6This category also includes those firms whose managers were directly appointed by the contract signer.

8
# **Discussion**

It is very impressive that Indonesia was able to achieve such a gain in test scores in math given the increased enrollment of disadvantaged children in the school system (see Figure 3). As enrollments in lower secondary schooling continue to increase, more and more students from families with less well-educated parents are entering the school system. For example, in 2003, the average level of education attained by the fathers of the 15-year-old students was 9.26 years, and for their mothers it was 8.30 years. By 2006, the average level of the fathers’ schooling had fallen to 9.09 years, while the mothers’ level had fallen to 8.16 years.

**Figure 3: Changes in Parental Education and Students’ Scores over Time**

<!-- Start of picture text --> 2003 2006<br>440 40 440 40<br>30 30<br>400 400<br>20 20<br>360 360<br>10 10<br>320 0 320 0<br>None Prim Low Sec Up Sec Univ None Prim Low Sec Up Sec Univ<br>PISA Mother schooling PISA Mother schooling<br><!-- End of picture text --> <!-- Start of picture text --> 2003 2006<br>440 40 440 40<br>30 30<br>400 400<br>20 20<br>360 360<br>10 10<br>320 0 320 0<br>None Prim Low Sec Up Sec Univ None Prim Low Sec Up Sec Univ<br>PISA Father schooling PISA Father schooling<br><!-- End of picture text --> Table 7 presents the variables that are listed in the 2006 dataset. The more institutional variables that we included in our analysis, the more interesting were the findings that emerged. Among other things, firing teachers, which is an indicator of school autonomy,
workers.<sup>19</sup> This concluding section outlines policy directions that could be tailored to any context, as we do for Bihar, for reform leaders and international partners to consider in their investments in public health systems. The following three elements characterize these policy directions:

- **Infuse messages into management meetings in health bureaucracies** : the Bihar survey found that across all cadres of health workers, there are regular meetings held with district and block-level management (75 percent of sub-center ANMs say they have weekly meetings; 86 percent of ASHAs say they have at least monthly meetings with the MOIC at the Block PHC), and a significant proportion of health workers are part of WhatsApp groups (39 percent of village-ANMs and 52 percent of block-ANMs say they belong to a health workers’ WhatsApp group.). Evidence-based messages about the need for professionalism and motivated service delivery can be crafted for sharing at meetings that are already happening organically and provide an institutionalized space for strengthening peer-to-peer norms.<sup>20</sup> In the Bihar and India context in particular, this involves a radical shift away from the culture of "scoldings" and disciplining, towards encouragement and recognition of health workers as professionals whose services are urgently needed for the good of society.

- **Newspaper and TV campaigns** : Bihar provides a context in which readership of Hindi-language daily newspapers provides a platform to share complex messages that require reading and discussion among people. As discussed above, newspaper reading is already happening as a way of finding out local, Bihar-specific news, and thus presents an organic platform that external agents can use to disseminate credible, databased messages. At the same time, the relative importance of TV viewing among the poorer groups of respondents in villages suggests a role for inserting Bihar-specific in-

> 19The survey on which this paper draws was designed to understand the management environment in which health workers perform their tasks, and did not have scope to address the important area of professional training. We thank
significant volume of imports, accounting for some 16 percent of the value of Western DRC imports. Given that there is no formal upriver trade (with CAR and Cameroon) left, this points towards non-negligible formal imports from Brazzaville. These imports could be of two kinds: overseas imports that have been unloaded at Pointe Noire (RC) or Douala (Cameroon) and transited through Brazzaville, or imports of goods produced in RC. However, we could obtain detailed trade data only for Kinshasa Beach, not for the freight port. Detailed data obtained from DGDA show only five small RC-originated shipments recorded for all of 2007, representing but a fraction of a percent of Western DRC imports. Recorded volumes of imports that have transited through RC are somewhat larger, but also account for well under one percent of Western DRC imports. The main recorded DRC import goods from RC are fuel, vehicles, building materials, cement and mineral water.<sup>10</sup>

> 9 For Kimpangu, the 2007 statistics report a single shipment (USD 660 worth of beans). Most trade with Angola clearly is informal and therefore goes unreported. For Kinshasa Beach, see also Table 3.

> 10 Some DRC imports from RC are not recorded by DRC customs but by the port authority in Brazzaville (see Table 5).

11
000|0.054|0.790|
|FAI (mil. USD)|469.338|2730.990|0.000|4.385|666.202|
|Lag K (mil. USD)|1835.101|17882.450|0.000|13.134|2054.840|
|Log(FAI)|2.355|2.797|0.000|1.478|6.502|
|Panel C: Weights||||||
|Sales Share (Lagged)|0.027|0.127|0.000|0.000|0.023|
|Capital Share (Lagged)|0.036|0.159|0.000|0.000|0.024|
|Export Share (lagged)|0.060|0.176|0.000|0.001|0.142|
|Panel D: Zeroes||Share||||
|I/K (FAI)||33.79 %||||
|I/K (FDI)||44.98 %||||

_Notes_ : There are 12,630 observations in our main sample. Panel A shows summary statistics for taxrelated variables. The (statutory corporate tax) rate differential term is defined as the weighted difference scaled by (100-host country statutory rate) and multiplied by _α_ , as in equation (4). The statutory rate and KF-METR are shown in percentages. Panel B shows summary statistics for investment variables. _I/K_ ratios are investment flow divided by lagged capital stock, for the type of investment indicated (foreign affiliate investment and foreign direct investment, respectively). The summary statistics for _I/K_ in the FDI dataset are associated with a different sample (N=17,442). Panel C summarizes the data used to construct the rate differential: weights by sales, capital and exports. Panel