rafmacalaba/data-use-mentions-extended huggingface dataset:
# Figure 19. SAR: Potential output growth

A. Potential output growth B. Potential output growth by different estimates <!-- Start of picture text --> Percent 2000-10 2011-21<br>8 2022-30 2000-21<br>6<br>4<br>2<br>0<br>EMDEs SAR<br><!-- End of picture text --> <!-- Start of picture text --> Percent 2000-21 2011-21<br>8<br>7<br>6<br>5<br>4<br>PFA Exp.<br>D. School closures<br><!-- End of picture text --> <!-- Start of picture text --> C. Within- and between-sector D. School closures<br>contributions to productivity growth<br>Percentage points Weeks<br>8 Within sector 40 Partial Full World<br>Between sector<br>6 35<br>30<br>4<br>25<br>2 20<br>0 15<br>10<br>5<br>0<br>SAR EMDEs Mar-Aug 2020 Sep 20 - Aug 21 Sep 21 - Mar 22<br>Sources: Asian Productivity Organization database; Groningen Growth Development Center database; ILOSTAT<br>database; OECD Structural Analysis database; Penn World Tables; UN Population Prospects; World Bank; World<br>Bank, Development Indicators database.<br>1990-99 2000-10 2011-16 1990-99 2000-10 2011-16<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar GDP (at average 2010-19 prices and market exchange rates) for the period 2011-21. A.B. Period averages of annual GDP-weighted averages.

A. Estimates based on the production function approach. Sample includes 53 EMDEs, of
dimensions. Thus, this approach assumes two poverty standards uˆM<sup>anduˆ</sup> OD<sup>,respec-</sup> tively for the monetary dimension and the other (non-monetary) dimensions. These two standards uˆM<sup>anduˆ</sup> OD<sup>areconsidered“equivalent”,asintheWorldBank’sMPM.43The</sup> comprehensive survey allows computing the “true” fraction of multidimensionally poor individuals, denoted by H<sup>ˆ</sup> .

We propose a method to construct an estimate H<sup>ˆˆ</sup> of H<sup>ˆ</sup> when the joint distribution between monetary and non-monetary dimensions is missing. That is, there is no comprehensive survey but rather two separate surveys, say one LSMS household consumption survey and one DHS or MICS household survey. The former survey allows computing the fraction of monetary poor individuals, which we denote by H<sup>ˆ</sup> M . The latter survey allows computing the fraction of other-dimensions poor individuals, which we denote by ˆ HOD. Some individuals are simultaneously monetary poor and other-dimensions poor. The only information missing to compute the “true” fraction H<sup>ˆ</sup> can be called the coefficient of overlap (c) between the two types of poverty. This coefficient captures “by how much” monetary poor individuals are more likely than those who are not monetary poor to be other-dimensions poor. Formally, c is the computed as the fraction of monetary poor individuals who are other-dimensions poor divided by the fraction of individuals who are not monetary poor but are other-dimensions poor In this setting, coefficient c summarizes all the relevant information on the missing joint distribution. If we knew the value for c, we could compute the “true” value for H<sup>ˆ</sup> through The proposed method consists in assuming a value cˆ for the unknown coefficient c. This allows estimating the “true” value
<!-- Start of picture text -->
-<br>fo)o<br>°<br>©<br>©<br>ge<br>c<br>3<br>°<br>N<br>:<br>o<br>2012 2013 2014 2015 2016 2017<br>HEE Youth Unemployment rate (% of youth labor force ages 15-24)<br>——— Unemployment Rate (% of total labor force ages 15-64)<br><!-- End of picture text -->

<!-- Start of picture text --> oO<br>5s aZ y,feaLea7 aTaTSaa<br>an<br>i“ =“<br>a<br>3347<br>£<br><<br>a8<br>g<br>fo]°<br>2012 2013 2014 2015 2016 2017<br>——— 15-24 yrs-old ———~—- 25-29 yrs-old — —~ 15-64 yrs-old<br><!-- End of picture text -->
as well as the intersection between both. By doing so, it aims to contribute to the literature on intrahousehold allocation between children.

Fourth, insights from this study are directly relevant for policy makers, development practitioners, civil society organizations in Ethiopia, and beyond. The study examines the current impact of public finance on children, and highlights areas where government spending has the largest impact on reductions in child poverty and inequality. Furthermore, the findings build the basis to analyze the distributional effects of future fiscal policies on children, as illustrated by a range of selected policy simulations.

The rest of the paper is organized as follows. Section 2 presents the methodology. Section 3 describes the data. Section 4 presents and discusses the results. Section 5 illustrates the kind of insights that can be generated via policy simulations on the basis of fiscal incidence analyses. Section 6 summarizes key insights and indicates further areas of research.

# **2. Methodology**

# **2.1.  Measuring fiscal incidence**

The analytical framework uses the CEQ methodology (Lustig 2018) as well as its child-specific version CEQ4C (Cuesta, Jellema, and Ferrone 2021) to estimate the distributional impact of fiscal policy on children’s wellbeing. The approach begins with calculating pre-fiscal and post-fiscal income concepts by assigning public transfers, spending, and taxes. Four income concepts are considered: market income, disposable income, consumable income, and final income (Figure 1). The analysis then estimates monetary and multidimensional child poverty and inequality at different income concepts.

In this study, the individual child is the unit of analysis. The construction of most other variables and income concepts closely follows those described in Ambel, Tesfaye, and Yonis (2022). Disposable income is proxied by consumption expenditure in the underlying household survey data. Other income concepts are therefore computed through backward and forward calculations. Individual level expenditure is estimated based on intra-household resource allocation (Belete, Menon, and Perali 2019; Calvi et al. 2020) and equivalence scales
Policy Research Working Paper 10739

# **Abstract**

This paper examines the role of firm dynamics in aggregate total factor productivity, job flows, and wage inequality in Ecuador. Utilizing a comprehensive employer-employee dataset, the paper documents firm dynamics and job flow patterns that are consistent with the presence of market distortions. Also, the paper identifies factor misallocation as the main contributor to Ecuador’s total factor productivity deceleration. Given these trends, the paper explores allocative inefficiency drivers through firm- and industry-level regressions. Firms in the top productivity quintile face distortive non-wage labor costs that are 3.7 times higher than the bottom quintile, after controlling for firm size and age. The findings also provide evidence of credit misallocation across firms. Additionally, industries with higher job mobility, credit access, and competition and lower nonwage labor costs, minimum wage incidence, and zombie firms demonstrate higher allocative efficiency. Moreover, worker-level regressions indicate that misallocation drivers explain up to 41 percent of wage inequality, with non-wage labor costs and product market frictions as distortions driving this inequality.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice and the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at fpatinopena@worldbank.org and eferro@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
# **1 Introduction**

Public debt statistics are a cornerstone of macroeconomic analysis. Investors, taxpayers, and academic researchers all have a keen interest in the level and composition of a country’s public debt. However, these statistics are subject to major limitations and incomplete reporting (World Bank, 2021; Horn et al., 2021). When Zambia and Chad sought debt restructurings in 2021, it took them more than six months to assemble comprehensive debt data and reconcile it with the records of their creditors (Estevão, 2021). Most famously perhaps, large revelations of previously unreported debts triggered major debt crises in Greece and Mozambique (Reinhart and Rogoff, 2011a,b; IMF, 2018). Despite the importance of hidden debt, there is little research that systematically measures its magnitude, characteristics, and effects.

We fill this gap by quantifying hidden debt by systematically tracking ex post revisions of debt figures across different editions (“vintages”) of the most widely used international debt statistics. The key idea is as follows: when previously unreported loans are reported, past debt statistics need to be revised. Tracking these revisions allows us to quantify the scale, characteristics, and timing of hidden debt accumulation and revelation. We apply this approach to a new and comprehensive dataset of the past 51 vintages of the World Bank’s International Debt Statistics that we digitize from archival sources all the way back to the 1970s. Our new approach and data allow us to systematically document the degree of under-reporting for more than 50 years of data and for up to 146 developing and emerging market countries.

Our empirical analysis makes three novel contributions to our understanding of hidden debt. First and foremost, we document that there is a pervasive under-reporting of public debts. We show that debt data revisions are systematically upward biased for almost all debtor regions and income groups. Across all countries and years, we identify USD 1 trillion in “hidden” sovereign borrowing that is added to debt statistics only in hindsight, more than
than women to have their preferences met by default without having to engage in household water management.

We define having **effective power through influence or persuasion** as not having direct control in the decision (1) despite desire to have procedural control or (2) because the desire not to be involved in decision-making stems from wanting to avoid penalties or other negative consequences ( _external_ or _introjected regulations_ ). Yet, behind the scenes, when they have a difference in opinion on how the decision should be made, they influence the decision-maker’s mind, so they are pleased with the outcome. This second form of effective power can entail high time and opportunity costs, as power is exercised indirectly within intrahousehold negotiations.

It should be noted that not all of those who are not involved in decision-making have effective power. Many individuals have no agency in these decisions.

# **3. Data and descriptive statistics**

Our data was collected through a household survey and a set of qualitative tools. The survey was administered in three primarily rural sub-counties in Kilifi County, Kenya (Kaloleni, Magarini, and Ganze). The household survey had two components: a household and an individual questionnaire. The householdlevel questionnaire was first administered to the primary respondent who makes the decisions and is the most informed about the household’s water choices. After administering the household-level questionnaire, each adult household member 18 years and older, including the primary respondent, was interviewed privately.<sup>i</sup> The individual-level questionnaire included detailed questions about who normally makes the decisions within the household regarding water collection and use: (1) which water source the household uses for drinking water, (2) how much can be spent on regular water expenditures—such as fees, transportation, costs for delivery, and (3) how the water is allocated and used in the household. It also included four questions that are typically asked in the Demographic and Health Surveys (DHS) regarding who normally makes the decisions
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Benchmarking air transport indicators for Mozambique and select other countries**

|Country|Mozambique|Tanzania|Zambia|South Africa|Zimbabwe|Angola|
|---|---|---|---|---|---|---|
|Total seats (per year)|1,819,117|3,694,171|2,010,641|45,789,157|1,533,406|2,272,173|
|Domestic|1,144,644|1,871,255|437,658|31,767,537|237,835|1,199,016|
|International travel within Africa|582,836|1,237,153|1,459,766|6,314,557|1,109,986|484,179|
|Intercontinental travel|91,637|585,763|113,217|7,707,063|185,585|588,978|
|Per capita seats|0.087|0.093|0.168|0.954|0.118|0.134|
|Herfindahl-Hirschmann Index—air<br>transport market (%)|31.5|9.8|17.5|16.7|30.2|33.3|
|Percent of seat-km in newer aircraft|57|79.3|63.8|83.8|71.4|59.7|
|Percent of seat-km in medium or<br>smaller aircraft|56.7|48.6|62.8|32.8|42.7|13.9|
|Percent of carriers passing<br>IATA/IOSA audit|100|33|0|33.3|0|0|
|FAA/IASA audit status
low power against the hypothesis of no seasonality – see the final column in each of Tables 3 and 4.

In summary, parsimonious seasonal models are likely to be preferable to the standard dummy variable procedure for estimating the extent of seasonality when data samples are short or seasonal processes are poorly defined. These are typical circumstances in data on prices for developing country food crops. These procedures substantially reduce the bias resulting from use of dummy variable estimators of the seasonal gap. Significance tests on the presence of seasonality remain correctly sized (i.e. they incorrectly reject the hypothesis of no seasonality in the expected proportion of cases) but they may have low power (they fail to correctly reject the hypothesis of no seasonality in a large proportion of cases). Their limitation is that they will perform poorly for crops in which there are two harvests per year.

# **5 Seasonality in African food crop prices**

The extent of seasonality in food prices is examined for seven African countries: Burkina Faso, Ethiopia, Ghana, Malawi, Niger, Tanzania and Uganda. Monthly price series for 13 crops and food products in local markets over the period 2000‐2012 were obtained from national statistical offices and from a private marketing agency in Uganda. The crops covered the main staple cereals (maize, millet, rice, sorghum and teff) together with cassava and a number of important fruits and vegetables, as well as eggs. The number of markets varies across countries. In four countries (Burkina Faso, Niger, Tanzania and Uganda), prices are reported both at the retail and wholesale level, although not always for the same marketplaces. For the other three countries there are only wholesale prices. This data set yields a total of 1,053 location‐food crop pairs. Table 5 provides more detailed information.

Prices are all expressed in nominal terms and local currency. There has been substantial inflation during the sample period in some of the countries. Deflation of the price of a major food staple by the local
- Lakner, Christoph, Daniel Gerszon Mahler, Minh C. Nguyen, Joao Pedro Azevedo, Shaohua Chen, Dean M. Jolliffe, Espen Beer Prydz and Prem Sangraula., "Consumer Price Indices used in Global Poverty Measurement ", World Bank Group Global Poverty Monitoring Technical Note, no. 4., September 2018.

- Lakner, C., Negre, M. and Prydz, Espen Beer, 2014. "Twinning the Goals: How Can Promoting Shared Prosperity Help to Reduce Global Poverty?," Policy Research Working Paper Series 7106, The World Bank.

- Minoiu, C. and S. Reddy, “Kernel density estimation on grouped data: the case of poverty assessment,” _Journal of Economic Inequality_ , 12(2), 163-89, 2014.

- Ncube, M., Z. Brixiova and Z. Bicaba, “Can Dreams Come True? Eliminating Extreme Poverty in Africa by 2030,” IZA Discussion Paper Series No. 8120, 2014:.

- Palma, J. G., “Homogeneous Middles vs. Heterogeneous Tails, and the End of the ‘Inverted-U’: It’s All About the Share of the Rich,” _Development and Change_ , 42(1), 87-153, 2011.

- Pinkovskiy, Maxim, and Xavier Sala-i-Martin. "Lights, Camera... Income! Illuminating the national accountshousehold surveys debate." The Quarterly Journal of Economics 131, no. 2 (2016): 579-631.

- PovcalNet: the online tool for poverty measurement developed by the Development Research Group of the World Bank, http://iresearch.worldbank.org/PovcalNet.

- Ravallion, M., “Growth, Inequality and Poverty: Looking Beyond Averages,” _World Development_ , 29(11), 18031815, 2001.

- Ravallion, M., “Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree?” _The Review of Economics and Statistics_ , 85(3), 645-652, 2003.

- Ravallion
Policy Research Working Paper 9533

# **Abstract**

Refugee camps are believed to represent safe havens for forcibly displaced persons, but studies looking at refugees’ quality of life in camps are few. This paper explores how Syrian refugees’ quality of life in camps in Jordan differs from that of Syrian refugees residing outside camps. Using data from the Syrian Refugee and Host Community Survey, the study measures life quality through indicators of subjective life experience and material living conditions. Data are analyzed using advanced statistical methods (difference-in-difference and propensity score matching) to control for selection bias that could skew estimates of causal effects. The results show that refugees living outside camps enjoy a higher quality of life than those living in camps. Out-of-camp refugees are less likely to live below the national abject poverty line or in overcrowded houses. They possess more household assets, are more satisfied with access to services, and report higher life satisfaction. Refugee camps appear to serve as safe havens for refugees who lack the capability to exit camps, and camps could be redundant for those who possess adequate capabilities and freedom to function in the urban and peri-urban areas.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The author may be contacted at cobi1@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
17

# Structural Equation Estimates

19. Estimates for two alternative formulations of the structural equations are given in Table 2. The first alternative includes the full set of independent variables. In the second alternative REXHP is omitted.

20. The value of R<sup>2</sup> is about .6 in all equations, which is high for cross section regressions, and indicates that the estimated equations explain over half of the total variation in the fertility and infant mortality rates. The signs of all coefficients are as expected with the exception of the coefficient on illiteracy which is negative in the fertility equation. Sivilar results for illiteracy have been obtained in other studies, especially where the illiteracy variable does not distinguish between male and female literacy. For example, Heller (1976) in his analysis of Malaysian household data found a positive coefficient on male literacy and a negative coefficient on female literacy. Cochrane (1979) surveyed studies relating fertility, literacy and education and concluded that above a threshold illiteracy rate of about 40-60%, literacy and small amounts of education appear to be associated with higher fertility. This phenomenon may be operating in Indonesia although in this case the illiteracy rate is below 40%.
ENIGH parameters generate smaller program effects compared to the results in Table 1 because all the parameters are a little smaller in size than ENCEL-generated parameter estimates. These results confirm the findings of Leite (2007) for Brazil, which show that estimations based on a more heterogeneous sample such as the one surveyed in the national representative PNAD leads to smaller parameter estimates. By estimating the same set of parameters for a subsample of homogenous households, Leite (2007) found a much higher impact on the enrollment rate and reduction of child work.<sup>30</sup>

**Table 3: Estimating Key Parameters from BFL model** Using Baseline 1997 and Enigh 1996 Surveys

|_Boys and Girls_|_Baseline - ENCEL_|_Enigh_|
|---|---|---|
||_8-17y.o._<br>_12-17y.o._|_12-17y.o._|
|α0|0.025<sup>******</sup><br>0.021<sup>******</sup>|0.018<sup>******</sup>|
||(0.008)<br>(0.008)|(0.009)|
|α1|0.025<sup>******</sup><br>0.021<sup>******</sup>|0.017<sup>******</sup>|
||(0.009)<br>(0.008)|(0.009)|
|α2|0.025<sup>******</sup><br>0.021<sup>******</sup>|0.018<sup>******</sup
large weather index insurance program with almost national coverage. In contrast to much of the existing evidence on index insurance, we are also able to observe effects several years after the start of coverage. There exist some data issues that negatively impact the precision and robustness of our results. These issues include, lack of payment data at the weather station level, missing data from some weather stations, and limited overlap between the set of insured municipalities and the set of municipalities represented in household expenditure and income surveys in any given year. Nevertheless, we believe that this analysis provides suggestive

> 13As mentioned earlier, this is per capita income for the three months prior to the ENIGH survey.

13
(767)|(0.392)|
|Control Mean|11138|22.2|14346|22.5|
|Sample Size|173|173|198|198|
|**Panel D: Firm Profits**|||||
||2019 Level|2019 I.H.S.|2020 Levels|2020 I.H.S.|
|Assigned to Treatment|-246|-0.335|-338|-0.875|
||(207)|(1.311)|(207)|(1.357)|
|Control Mean|1020|16.64|1118|17.06|
|Sample Size|200|200|200|200|
|**Panel E: Labor Productivity (Sales pe**|**r Worker)**||||
||2019 Level|2019 I.H.S.|2020 Levels|2020 I.H.S.|
|Assigned to Treatment|-2.7|0.376|-39.3|-0.135|
||(22.2)|(0.263)|(31.1)|(0.100)|
|Control Mean|170|19.04|223|19.52|
|Sample Size<br>Notes:<br>Notes: Robust standard errors in par<br>Panel B uses monthly data and cluste<br>Panels A and B use data on formal em<br>from phone calls and the RUES.<br>Panels C through E use data from the|162<br>entheses. *, **, *** den<br>rs standard errors at th<br>ployment from the PIL<br>RUES. I.H.S. denotes in|162<br>ote significance at t<br>e firm level.