rafmacalaba/data-use-mentions-extended huggingface dataset:
using its assets. Low ROA indicates low productivity and thus potential vulnerability to shocks and financial difficulties. Jordan reports healthy average profitability of around 6%, which compares well with Israel and Turkey, but below that reported in Morocco and Egypt.

Leverage is one of the key measures of corporate vulnerability. High leverage suggests higher risk of insolvency in response to shocks that reduce the value of assets relative to liabilities. Two main measures of leverage are total debt to assets and total liabilities to assets.<sup>2</sup> In Jordan these ratios are relatively low. In fact, these ratios are the lowest of all the comparator countries, only 24% for total debt and about 30% for total liabilities, scaled by assets. While these ratios do not suggest high vulnerability in Jordan, they might point to difficulty with obtaining debt financing, which affects even the largest corporations that are represented in the Worldscope.

While total leverage shows the overall indebtedness of the company, the maturity structure of the debt is also an important factor in determining corporate vulnerability. Shortterm debt makes companies more vulnerable to hikes in interest rates and credit crunches that may reduce creditor’s willingness to roll over short-term debt. In Jordan firms have very high proportion of short-term leverage. The ratio of current liabilities to total liabilities is above 80%, which is the largest of all comparator countries, closely followed by Morocco. The ratio of shortterm debt to total debt is also quite high – at 68%, which is just below Morocco at 70% and higher than in other comparator countries.

While the total levels of debt and leverage do not indicate high vulnerability in Jordan, high proportions of short-term debt and short-term leverage might be a source of concern from stability perspective (as they make companies more vulnerable to shocks) and from developmental perspective (as inability to raise long-term financing might slow down the potential growth).

> 2 To increase comparability with Stock exchange data discussed in the next section, the total debt is estimated as
inflation expectations. Surveys of inflation expectations in EMDEs typically have smaller samples than those in advanced economies, but the number of EMDEs included in Consensus Economics’ surveys has increased over time, from seven in 1990 to 52 in 2018.<sup>5</sup> Differences between surveys of households or firms and surveys of professional forecasters . On average, households’ and firms’ inflation expectations are higher than professional forecasters’ expectations in advanced economies and EMDEs (Figure 1). The volatility of households’ inflation expectations is also larger than that of professional forecasters’ expectations. Households’ beliefs about past inflation are found to be a strong predictor of their inflation expectations (Jonung 1981; Malmendier and Nagel 2016). Households’ inflation expectations are thus more backward looking than professional forecasters’ expectations.<sup>6</sup>

> 2 Most survey results are presented as median responses. Discrepancies among respondents can be informative as a proxy of inflation uncertainty (Mankiw, Reis, and Wolfers 2003; Miles et al. 2017).

> 3 For the European Union, a data set on inflation expectations has been collected by the European Commission since 2003. Although it has been used for research purposes, it has not yet been published (Arioli et al. 2017). Some central banks (for example, those of China, Poland, and Romania) release survey results showing the percentage of respondents who expect inflation to change.

> 4 In addition, Germany’s Ifo Institute has provided data on five-year-ahead inflation expectations for more than 70 countries since the end of 2014.

> 5 The International Monetary Fund’s World Economic Outlook has the broadest country coverage of longterm inflation projections (39 advanced economies and 154 EMDEs).

> 6 Kumar et al. (2015) and Kabundi, Schaling, and Some (2015) document that in New Zealand and South Africa, some firms do not understand the central bank’s objective function. Hence, even if professional forecasters’ expectations are well anchored
-|---|
|ax category|ETB (in<br>millions)|Share of tax<br>revenue (%)|Share of<br>GDP (%)|ETB|Share of tax<br>burden (%)|
|Total taxes|268,457|100.0|13.5|680|100.0|
|Direct taxes|115,858|43.2|5.8|208|30.6|
|Personal income tax|41,203|15.3|2.1|149|21.9|
|Business profit tax|59,407|22.1|3|24|3.5|
|Land use fee and<br>agriculture income tax|708|0.3|0|23|3.4|
|Rental income tax|2,138|0.8|0.1|5|0.7|
|Other direct taxes|12,403|4.6|0.6|7|1.1|
|Indirect taxes|152,600|56.8|7.7|472|69.4|
|Domestic indirect taxes|77,774|29.0|3.9|414|60.9|
|Import duties<br>**_Source:_**_Authors’ calculations; D_|74,826<br>_ata from the Mi_|27.9<br>_nistry of Finance._|3.8|58|8.5|

**_Source:_** _Authors’ calculations; Data from the Ministry of Finance._ **_Note:_** _Import duties include customs, surtax, VAT, and excise on imports. ETB = Ethiopian Birr._

Ethiopia’s 2018/19 public spending with child-relevant components is shown in Table 3. Thirtynine percent of government spending goes towards social development, followed
Policy Research Working Paper 10542

# **Abstract**

To what extent has the learning progress of school-aged children slowed during the COVID-19 pandemic? A pre-registered analysis of the first international assessment to be published since the pandemic is conducted to estimate the impact of COVID-19 on student reading. The effect of closures on achievement is modeled by predicting the deviation of the most recent results from a linear trend in reading achievement using data from all rounds using data from 28 countries in Europe and Central Asia. Reading scores declined by an average of 20 percent of a standard deviation, equivalent to just less than a year of schooling. Losses are significantly larger for students in schools that faced relatively longer closures. While there are no significant differences by sex, it is shown that lower-achieving students experienced much larger losses.

This paper is a product of the Education 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 hpatrinos@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
, the stocks of physical and human capital, and the composite index of infrastructure capital, all (except for human capital) measured in logs per worker. As a preliminary step, we remove the cross-sectional means from the data, to render the disturbances cross-sectionally

> 20 As noted by Canning (1999), infrastructure appears twice in (1): first as _z_ , and then as part of overall physical capital _k_ . Hence the total elasticity of output with respect to infrastructure capital can be approximated as     , where  is the share of infrastructure in the overall physical capital stock. Evaluation of  requires data on the price of infrastructure, which are not widely available. Nevertheless, calculations based on the data of Canning and Bennathan (2000) suggest that it is a small number. For the countries in our sample with available data, its median is 0.08, and its standard deviation is 0.05.

9
(2006), Canova and Gambetti (2009), Cogley et al. (2010), Koop and Korobilis (2013), Del Negro and Primiceri (2015), and Antonakakis et al. (2019).

The model is specified as: where _Yit_ is an _Nx_ 1 vector of endogenous variables, _Xit_ is a matrix of lagged dependent variables including an intercept, _B_ is a matrix of time-varying coefficients, and _εt_ is a time-varying independently and identically distributed error.

The VAR includes four variables: real output, consumer inflation, real policy rates–defined as the policy rate less expected inflation–and the real effective exchange rate. The models are estimated on quarterly data from 2000Q1 with two lags. The benefit of the TVP-VARs is that it applies less structure than the Taylor Rule and Laubach and Williams (2003) model to expected economic relationships.

In order to generate neutral real interest rate estimates from the TVP-VAR, five-year ahead iterated forecasts of the real interest rate are used as in Lubik et al. (2015); that is _rt_<sup>_∗_=</sup><sup>_rt_+20.13</sup>

# **6 Data**

Table 2 describes the data, sources and transformations used in this paper. All data are quarterly and collected as available, therefore, time spans depend on country-specific availability. The modeling strategy employed uses switches which turn on and off portions of the models for each country depending on data availability. Not all data is seasonally adjusted. When only non-seasonally adjusted data is available, the data is seasonally adjusted by Haver or using X-13ARIMA-SEATS.

In the Taylor rule model, inflation expectations are used instead of contemporaneous inflation as the variable of interest to the central bank. This approach limits the country choice to only those that have consensus forecasts available and from 2005 onwards. Since consensus forecast reports fixed event forecasts every month that do not account for the time dimension of forecasts appropriately, the fixed horizon transformation in Siklos (2013
hypothesized as follows:

- _Hypothesis 1 (Extortion to Fear):_ Deportees who were extorted while migrating are more likely to avoid a range of situations out of fear in their origin country compared to deportees who were not victims of such abuse.

- _Hypothesis 1a (Extortion to Economic Hardship):_ Deportees who were extorted while migrating are more likely to experience economic hardship in their origin country than those who were not extorted.

- _Hypothesis 2 (Fear to Engagement):_ Deportees avoiding activities in their origin country because of fear are less likely to be politically engaged compared to those who are not avoiding activities out of fear.

- _Hypothesis 2a (Economic Hardship to Engagement):_ Deportees who experience economic hardship in their origin country are more likely to be politically engaged than deportees who do not experience economic hardship.

If fear and economic hardship both mediate the relationship between extortion and political engagement, a final hypothesis should concern which mechanism plays a larger role. If the emotional consequences of extortion are larger than the financial ones, extortion should be correlated with decreased rather than increased levels of political engagement. However, we have no prior reason to believe that one mechanism is more or less important than the other. Thus, any conclusions about the overall relationship between extortion and political engagement is exploratory.

# **4 Research Design: Data and Methods**

## **4.1 Deportee Survey**

We employ data from an original survey of recent migrants deported from from the United States and returned to Guatemala (“deportees” or “returnees”). The project was approved by Duke University’s IRB in protocol 2020-0075. Beginning in October 2019, we partnered with RTI International and Te Conecta, a Guatemalan NGO, to implement a face-to-face survey of newly arrived deportees at the Air Force airport in Guatemala City. This airport is the main arrival point for deportees sent to Guatemala and typically receives 3-5 planes of deportees 4 or 5 days per week. The first stage of the survey was
Hence, the concern is increase in inundated area and inundation depths due to climate change will further increase the inundation risks of roads.

This paper has estimated the length of roads (km) exposed to additional inundation risk due to climate change for different types of roads from the GIS overlays of the maps of additional inundation risk zones with the existing road maps for each category. Bangladesh already has one of the densest road networks in the world, leaving little room for extensive growth in the road network in future. In absence of long term planning for additional new roads, this exercise has assumed that total lengths of roads in Bangladesh will increase by another 25% over the existing stock by 2050. The assumed 25% expansion of the road network is applied uniformly across the existing network.<sup>24</sup> Table 5 summarizes the total kilometers of roads likely to be exposed to various degrees of additional inundation due to climate change. Computation indicates less than 2% of roads in 2050 will be subject to more than 1 meter of additional inundation due to climate change, while almost all (87%) will be inundated by up to 0.5 meters due to climate change. (See Table 5.) The total cost of raising roads to avoid further inundation due to climate change has been calculated using DBS=Wearing Course 50 mm thick on average (Taka 8,350 per m<sup>3</sup> ), DBS-Base Course 50 mm thick on average (Taka 7,899 per m<sup>3</sup> ), Aggregate Base Type I- 100 mm (Taka 2,819 per m<sup>3</sup> ), Aggregate Base Type II- 100mm (Taka 2,232 per m<sup>3</sup> ), roadway excavation (Taka 184 per m<sup>3</sup> ) and earth fill from borrow pit (Taka 124 per m<sup>3</sup>
# **4 Empirical Strategies**

## **4.1 Regression Discontinuity**

We first rely on a Regression Discontinuity (RD) framework to identify a causal effect of FA on decisions regarding registering to vote. We exploit the discontinuity in FA eligibility for people around the threshold, since people just below the eligibility threshold, the first bracket of the SISBEN score, were able to enroll in FA, while people just above were not. Moreover, the cutoff for the first bracket of the SISBEN score does not determine eligibility to other major social programs except for a child feeding program given to children between 6 months and 5 years old, which is an in-kind daily transfer that does not coincide specifically with the payment of FA, therefore this is not expected to invalidate our empirical strategy. In section 5.3 we discuss the robustness of the results when potential beneficiaries of the child feeding program are excluded from the sample of analysis.

We need to validate several assumptions for the RD strategy to work in this context. Specifically, if the eligibility indicator affects registration to vote only through enrollment in FA, then two necessary conditions should hold. First, there should be no bunching in the distribution of the _SISBEN_ score around the eligibility threshold. Figure 2 shows the histograms for all the individuals in the SISBEN data base (top graph), and in our matched sample for men and women (bottom left) and for women only (bottom right). By design of the proxy-means test algorithm, the distribution is bimodal and the overall distribution is very similar to the one in our matched sample. Furthermore, the density is higher above the eligibility threshold<sup>12</sup> than below it, which is contrary to the idea of manipulation of the score in order to become eligible for FA. Neither distribution shows bunching at the eligibility threshold. Second, as Figure 3 shows, other variables which are not affected by the program are continuous at the eligibility

> 12Normalized to a value of
We ensure representativeness of our worker panel database by showing that the demographics and job characteristics of workers in the 10% worker random sample are close to those of workers in the complete RAIS database (see Panels A and C in Appendix Table A1).<sup>16</sup>

# **III. Impact of Firms’ GFC Foreign Shocks on Workers**

## _A. Empirical Design_

Our analysis tracks workers over time after the GFC, comparing the evolution of labor market outcomes for workers in firms that experience a larger versus a smaller adverse foreign shock due to the GFC. Our measures of firm foreign shocks due to the GFC - henceforth designated as ‘GFC firm shocks’ - are constructed as a combination of aggregate shocks with measures of firms’ shock exposure. For the aggregate shocks we exploit quasi-experimental variation in foreign demand across destination countries due to lower GDP growth caused by the GFC. For the firms’ shock exposure, we consider pre-GFC firm export portfolio weights across destinations built from customs data.<sup>17</sup> We define GFC firm shocks as firm-specific exportweighted destination market GDP decline given by: where _j_ is a firm, _d_ an export destination, _GDPgrd_ 2008 _t_ corresponds to the real GDP growth rate in destination _d_ between 2007 and 2008, and _wjd_ 2007 is the share of firm _j_ exports to destination _d_ in total firm _j_ exports in 2007.<sup>18</sup> The minus sign included in Equation (1) eases interpretation by allowing to capture the impact of a _decline_ in GDP in the firm’s destinations as a result of the GFC. Higher values of the GFC firm shock indicate that the firm experienced a more adverse demand shock induced by the GFC. For example, a firm with a shock equal to 1 (i.e., its destinations’ GDP declined on average by 1%) is more adversely affected by the GFC than a firm with a shock equal to -2 (i.e.,
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data

# _B. Prediction of True Sales_

The main objective of this paper is to estimate a firm’s true sales and therefore the extent of underreporting in sales to the tax office and in the survey. Prediction of the latent variable true sales in the MIMIC model can be done conditional on either the causal variables only or on both and the indicators.<sup>32</sup> Previous MIMIC empirical literature predicts the latent variable(s) conditional on the causal variables _X_ only (e.g., Giles 1999a, Chaudhuri _et al_ . 2006, Dell'Anno _et al_ . 2007). In order to increase precision, we choose to predict conditionally on both the causal variables _X_ and the indicators and and calculate<sup>33</sup> Next we calculate the unconditional underreporting to the tax office and in the survey by each firm in our sample using the formula , . The results are reported in Table 4.

Underreporting to the tax office is on average larger than in the survey (38.6% versus 11.9%). With respect to underreporting to the tax office, firms that are small (42.8%), firms that are located in Darkhan or Edernet (46.6% and 46.2%), and firms that do pay bribes and/or provided informal gifts when dealing with government authorities (41.9%), tend to underreport significantly more than other firms. Also, and surprisingly, firms in the construction sector tend to underreport less (33.9%). Firms facing credit constraints underreport slightly more on average than firms without such constraint, but the difference is not significant (39.9% v. s 37.8%).

32 Joreskog and Goldberger (1975) derived the formula of prediction conditional on both causal variables and indicators. But since we impose an inequality constraint, their formula for the conditional expectation will have
**Figure 3. Cost per calorie by consumption vintile among the bottom half of the distribution**

<!-- Start of picture text --> 0.006<br>0.005<br>0.004<br>0.003<br>0.002<br>0.001<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Vintiles (Bottom half of the distribution)<br>Cost per calorie (R$/kcal)<br><!-- End of picture text --> _Source:_ Own calculations using POF 2017/18 data.

**Table 12. Poverty lines using 2nd and 3rd consumption deciles as reference group**

|**Total Poverty**|**Food**|**Lower Total**|
|---|---|---|
|**Estimation**|**Poverty**|**Poverty**|
|Parametric|R$251|R$443|
|Non-parametric|R$251|R$441|

_Source:_ Own calculations using POF 2017/18.

# Poverty lines based on alternative treatment of items in “lumpy” modules

In section 3 we described our approach to exclude lumpy items and relatively infrequent expenditures from the consumption aggregate used to determine the reference population, as well as from the nonfood component when estimating the total poverty line. In our main specification we exclude expenditures made on ceremonies, jewelry, property, vehicles, and construction, while in the following categories of goods and services, we exclude items that have average expenditures above R$500: toys and recreational goods and services, domestic electrical appliances, domestic nonelectrical appliances, and furniture. In a variation of this main specification, we exclude all toys and recreational goods and services; domestic electrical appliances; domestic nonelectrical appliances; and furniture, regardless of the average amount spent on
sup>_∗∗∗_</sup>|||3.067<sup>_∗∗∗_</sup>|
|Hansen _J_|||31.644|||60.344|
|AR(2)|||-0.169|||0.429|
|Estimator|OLS|RE|S-GMM|OLS|RE|S-GMM|
|Instruments|||42|||74|
|_N_|682|682|682|633|633|633|

> * Growth rates calculated from GDP data in real 2000 U.S. dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are fiveyear averages. All variables are in log form. Heteroskedasticity and autocorrelationrobust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

Indeed, per capita income appears to be strongly correlated with growth polarity. In some ways, this is not entirely surprising: economies that have historically been significant drivers of global growth are also generally the ones that have become developed, and this is reflected in the strong positive coefficient on the GDP per capita variable. The magnitude of this effect is relatively small, however, despite its strong statistical significance: a one percent increase in per capita income raises the polarity measure by about 0.1 percent.

Across the three specifications, a one percentage point increase in the size of government leads to a decrease in the polarity measure that ranges [0 _._ 9 _,_ 1 _._ 5] percent. This
_SOEs_|Firms where municipal governments hold 10 percent or more<br>stakes.|

> 6 Subsidies in the dataset are presented as a single continuous variable with the amount granted for any of the following objectives: (i) employment creation; (ii) renewable energy and (iii) fossil fuels. The variable called “subsidies” does not allow to differentiate the types of instruments used (e.g. grants, tax exemptions, tax deferrals, etc).

6