rafmacalaba/datause-encoder-data huggingface dataset:
conservative measure. If interest coverage falls below 0.75, it is highly likely that the firm will experience financial distress. The threshold of 0.75 is used in Jones and Karasulu (2006) who argue that it provides a better mapping to the NPL ratios by banks. Among the 18 firms available in the Worldscope no firm has interest coverage below 0.75, while 2 firms have relatively low interest coverage between 1 and 1.5. Based on the interest coverage ratios firms in Jordan appear healthier or comparable to other countries.

Finally, Altman’s Z-score is an aggregate measure of vulnerability, which takes into account several key ratios used to predict corporate default in the US and other countries.<sup>3</sup> In Jordan the average Altman Z-score ratio for the 18 non-financial companies in Worldscope is the highest in the region, which means these are on average the healthiest firms relative to comparable countries.

Based on the Wordscope data the benchmarking exercise shows that firms in Jordan show relatively low vulnerability. The one point of concern and further investigation are high proportions of short-term debt.

However, this analysis is limited to 18 Jordanian firms available in Worldscope. The results are thus subject to small sample concerns. These 18 firms are the largest of the listed nonfinancial firms – they comprise about 60% of total assets of all non-financial firms listed on ASE. Therefore they are not very representative of the total population of firms listed on ASE (although from the systemic perspective these firms are the most important). Below we perform more in-depth analysis for all listed firms on ASE.

# **3. In-depth Analysis of Corporate Vulnerability in Jordan**

Data on public firms available from ASE (Amman Stock Exchange) allow for more in-depth analysis of corporate vulnerability across different sectors, firm size and over time.

> 3 Altman’s Z-score is given by the formula : Z-score=0.717*WC_TA + 0.847*RE_TA+ 3.107*ROA+ 0.420*BVE_TL+0.998*S_TA where, WC_TA is working capital (defined as current assets minus current liabilities over total assets), which is another measure of liquidity, ROA is return on assets, RE_TA is the ratio of retained earnings over assets (which reflects cumulative performance over time), BVE_TL is the ratio of book value of equity over total liabilities (a measure of indebtedness similar to the ratio of total liabilities to total assets) and S_TA is a ratio of total sales over total assets. See Altman(2000) and Altman and Hotchkiss (2005).

6
**Table 89. Family and repro health outcomes (ITT), disaggregated for women, 2010, 2011 and 2012 cohorts**

||Baseline mean for|Pooled 2<br>IP|010‐2012 Cohorts<br>SW model|||
|---|---|---|---|---|---|
||young women|Baseline mean for women|Young women|Older women|Difference|
||[Std Dev]|[Std Dev]|(1)|(2)|(3)|
|Desired number of children|1.968|2.501|‐0.094***|‐0.038|‐0.056|
||[0.504]|[0.965]|(0.035)|(0.043)|(0.055)|
|Pregnant (1= Yes), if married|0.044|0.025|0.030|0.001|0.029|
||[0.205]|[0.157]|(0.021)|(0.014)|(0.025)|
|Number of children|0.468|2.221|‐0.014|‐0.040*|0.026|
||[0.812]|[1.265]|(0.026)|(0.023)|(0.035)|
|Ever argue with spouse/|0.751|0.849|‐0.022|0.013|‐0.035|
|partner, if applicable (1=Yes)|[0.433]|[0.358]|(0.050)|(0.031)|(0.063)|
|Use any type of contraception|0.645|0.795|‐0.008|0.023|‐0.031|
|(1=Yes), if sexually active|[0.479]|[0.404]|(0.063)|(0.027)|(0.068)|
|HIV Knowledge score (0‐3)|1.173|1.000|0.128*|‐0.029|0.157|
||[0.761]|[0.773]|(0.072)|(0.094)|(0.124)|
|Household food insecurity (0‐|0.514|0.528|‐0.084|‐0.034|‐0.050|
|4)|[0.767]|[0.761]|(0.057)|(0.055)|(0.087)|
|Weekly protein consumption|0.389|0.381|‐0.075|0.027|‐0.102|
||[0.488]|[0.486]|(0.047)|(0.051)|(0.069)|
|Household receives|0.231|0.304|‐0.026|0.073*|‐0.098|
|remittances (1=Yes)|[0.422]|[0.460]|(0.045)|(0.042)|(0.061)|
|Household received more|0.118|0.121|0.006|0.060*|‐0.054|
|remittances than last year<br>(1=Yes)|[0.323]|[0.327]|(0.033)|(0.035)|(0.047)|
|Clustered Standard Errors|||Yes|Yes|Yes|

Notes: Standard errors (reported in brackets) clustered at the event level where possible. *,**, and *** denote significance at the 10% level, 5% level, and 1% level. Younger women (aged 16 to 24) compared to older women (age 25 to 35).

41
<okay – sg>

**Resolving the African Financial Development Gap: Cross-Country Comparisons and a Within-Country Study of Kenya**<sup>∗</sup>

**Franklin Allen, Elena Carletti, Robert Cull, Jun Qian, Lemma Senbet, and Patricio Valenzuela**

_Forthcoming in NBER Volume on African Economic Successes_ (University of Chicago Press; eds., S. Edwards, S. Johnson, and D. Weil)

**Keywords:** Africa, Kenya, finance and growth, population density, Equity Bank, financial access. **_JEL_ Classifications:** O5; K0; G0.

> ∗ Allen is from the University of Pennsylvania, Carletti is from the European University Institute, Cull is from the World Bank, Qian is from Boston College, Senbet is from the University of Maryland, and Valenzuela is from the European University Institute. We thank the Financial Sector Deepening Trust Kenya (FSD Kenya), and in particular David Ferrand, for providing the household surveys, and Samuel Makome and John Staley for providing information on Equity Bank. We appreciate helpful comments from Patrick Alila, Meghana Ayyagari, Thorsten Beck, Shawn Cole, Sankar De, Simon Johnson, Marcella Lucchetta, Randall Morck, Chukwuemeka Okoye, Daniel Sarpong, Moses Sichei, Philip Strahan, and seminar/session participants at Harvard Business School, Wesleyan University, the ERD Workshop in Accra, the Financial Intermediation Research Society meetings in Prague, the NBER African Successes 2011 conference in Zanzibar, and the Summer Research Conference at the Indian School of Business. We are grateful to Giulia La Mattina, Sailu Li, and Mircea Trandafir for excellent research assistance, and NBER and the authors’ respective institutions for financial support. We are responsible for all the remaining errors.
# **7 Exploring Similarities in Productivity Dynamics**

In this section, we explore the contributions of the identified technology shocks to labor productivity growth and contrast them with the contributions of non-technology shocks such as the aboveidentified business-cycle investment shock. We first examine the historical decomposition of labor productivity growth in advanced economies and EMDEs, highlighting the importance of each innovation to productivity dynamics over the sample period. We then explore the synchronization of labor productivity across economies to gauge the degrees of spillovers between countries and across regions, and the extent to which they are driven by technology shocks or cyclical factors.

## **7.1 Historical Decomposition: Comparing the Drivers of Productivity**

Several distinctions have already been explored between the effects of technology and non-technology drivers of productivity. Using the SVAR, the contribution of technological developments to labor productivity can be separated from the contributions of non-technological demand factors.

Historical decomposition of labor productivity growth can be written as a function of the structural shocks identified through the spectral identification _εt_ , initial condition _X_ 0 (which accounts for the lack of data prior to the start of the sample), and the constant, _C_ .

In the decomposition shown in Figure 9, the identified technology shock, initial condition, and constants are included in the technology category, given that they reflect average rates of growth and persistent effects from initial conditions. The effects of all other shocks are included in the non-technology category. The estimation used for the historical decomposition includes labor productivity in growth rates, rather than in log-levels as in the estimation of impulse responses. This is because the effects of initial conditions can be substantial in I(1) or highly persistent processes such as labor productivity levels. In the estimation using growth rates, the effects of the initial condition are minimal given the stationary nature of productivity growth.

17
**Table 12. Trust in leaders, local officials, and government**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Project<br>Leaders|LC3<br>Chairperson|Sub-county<br>Bureaucrats|LC5<br>Chairperson|District<br>Bureaucrats|Central<br>Government|
|Training|0.008|0.062|0.080|0.033|0.040|0.105***|
||[0.044]|[0.059]|[0.049]|[0.063]|[0.058]|[0.037]|
|Scorecard|-0.009|0.023|0.013|0.030|-0.033|0.094**|
||[0.053]|[0.067]|[0.057]|[0.074]|[0.066]|[0.043]|
|Training x scorecard|-0.054|-0.095|-0.127*|-0.061|-0.095|-0.129**|
||[0.064]|[0.083]|[0.070]|[0.093]|[0.085]|[0.052]|
|Training + Scorecard + interaction=0|0.215|0.866|0.470|0.964|0.107|0.059|
|Training=Scorecard|0.687|0.519|0.177|0.964|0.210|0.765|
|Control means|3.693|3.225|3.382|3.044|3.377|3.647|
|N|3,845|3,836|3,822|3,808|3,837|3,831|
|R-squared|0.119|0.141|0.097|0.083|0.102|0.113|

Notes: This table reports the OLS regression results for the treatment effect on individual-level outcomes at the final endline survey. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

48
# COVID-19, School Closures, and Student Learning Outcomes: New Global Evidence from PISA

**Maciej Jakubowski Tomasz Gajderowicz** University of Warsaw University of Warsaw

**Harry Patrinos**<sup>1</sup> World Bank

_JEL No_ : I19, I20 _Keywords_ : COVID-19; learning loss; student achievement; PISA; international large-scale assessments

> 1 Maciej Jakubowski, Faculty of Economic Sciences, University of Warsaw, Warsaw, Poland (mjakubowski@uw.edu.pl); Tomasz Gajderowicz, Faculty of Economic Sciences, University of Warsaw, Warsaw, Poland (tgajderowicz@wne.uw.edu.pl); Harry Anthony Patrinos, World Bank, Washington DC, USA (hpatrinos@worldbank.org).
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Alderman (2004) deal with the additional impact of nutrition education over the more general impact of schooling.

This paper focuses on the role of income growth in combination with two types of nutrition interventions in northwestern Tanzania, community driven supplementary feeding of young children, feeding posts, and child feeding carried out in crèches run by an international NGO, Partage. The implementation of these programs was likely to be an emergency response yet these crises were not directly issues of food scarcity. In any case, the study does not assess the impact of the provision of food _per se_ , but of the package of services, comprising of the provision of food, awareness creation, day and medical care, that accompany the intervention.

The current study looks at anthropometric measures of child nutritional status. We study the degree to which increases in resources at household level and participation in nutrition programs contribute to the reduction of child malnutrition. Other determinants of nutritional status, such as access to health care, parental human capital and environmental factors are also considered. Our study examines the impact of the availability of program interventions on the nutritional status of children rather than the impact of participation itself. While both approaches convey useful information, Heckman, Lalonde, and Smith (1999) observe in their review of econometric methodologies for evaluation that often it is the former that is of policy relevance.

The remainder of the paper is as follows. Section 2 describes the data set used. Section 3 illustrates the children’s health status in the Kagera region of Tanzania. Section 4 presents the econometric specification used and the estimated results according to different specifications. Section 5 continues with policy implications that the analysis implies. Section 6 concludes the paper.

# **2. The Data Set**

The data used for this study come from a longitudinal living standards survey of households conducted in Kagera, a region of Tanzania with comparatively high adult mortality in the first decade of the AIDS epidemic. The region is located to the west of Lake Victoria and borders the Rakai district of Uganda to the north, and Burundi and Rwanda to the west.

2
Figure 1 formalizes the distributional effect of bribery on service provision. We assume for simplicity that there are only two groups of citizens that differ in terms of income (low and high income). We also assume that the low-income group has a more inelastic demand for service curve than the high-income group. If public officials are able to impose a ‘bribery tax’ the cost of the service for the citizens increases, and the demand declines. However the change in demand will depend on the elasticity of the demand curve.

**Figure 1- The effect of bribery**

<!-- Start of picture text --> Price of<br>services<br>Supply<br>Demand<br>high income<br>Demand low<br>income<br>Quantity of services<br><!-- End of picture text --> We explore the distributional consequences of poor governance using household data from Peru. We focus on two different aspects of poor governance: bribery and discouragement. First, we identify in our sample the citizens who paid a bribe to obtain a service, and the citizens who were discouraged. We then observe the distribution of bribery and discouragement by income. Table 2.1 describes the percentage of income spent on bribes by households, who were asked to pay a bribe while trying to obtain a service. The data indicates that bribery tends to act as a regressive tax for most public services in Peru, penalizing low-income citizens more than higher-income ones.<sup>5</sup> <Table 2.1 around here> Bribery can however affect service delivery also indirectly, by limiting the quantity of service available or reducing its quality. At this stage we choose to focus only on the quantity effect.<sup>6</sup> Table 2.2 reports the distribution of discouraged users by level of income. For basic services, poor governance affects low-income citizens more than high-income ones. Such regressive effect appears to be significant especially for the most basic public services, like water, police, hospitals, schools, and municipal services (area de rentas). This suggests that

> 5 The observed regressiveness of bribery appears to be robust to different sample of respondents.  We obtain very similar results using the complete sample of households and the sample of households who contacted the agency, were asked to pay a bribe and decided to pay a bribe.

> 6 We plan to explore further the link between bribery and quality of service delivered using additional data available from the survey.

5
public services. The time tax is a proxy for how burdensome government red tape is.

- The cost of _inspections_ is measured as the number of days in the previous year spent on inspections and required meetings with government officials. Higher numbers of inspections lower performance because more inspections provide more opportunities for rent seeking; it can generally be assumed that the more

inspections there are, the more likely they are to be rent seeking activities.

**Table 1 Mean Indicators of Rent Predation by Country**

|Indicator|Kyrgyz<br>Republic|Moldova|Poland|Tajikistan|Uzbekistan|
|---|---|---|---|---|---|
|Inspections (days)|32.1|21.7|16.8|12.9|15.2|
|Bribe tax (percent)|2.1|1.2|0.4|1.6|0.5|
|Time tax(percent)|8.3|3.1|9.4|2.4|8.3|

_Note:_ See text for definitions of indicators. _Source:_ World Bank, Business Environment and Enterprise Productivity Extension Survey, 2003.

The Kyrgyz Republic appears to perform the worst on the rent predation measure, with the highest values for inspections and the bribe tax (table 1). Poland has the lowest bribe tax but the highest time tax. Still, it is difficult to say that any one country suffers from the highest level of rent predation. It has been suggested that there may be large measurement error because in some countries managers are unwilling to talk about government rent seeking. However, the interview teams provided no evidence of systematic measurement error.

# _Infrastructure_

The infrastructure measure indicates the effect of inadequate infrastructure on firm performance. A composite measure of the average number of days firms experienced 12
however to baseline dropouts only. Behrman, Parker and Todd (2009) find that _Progresa_ decreases likelihood to migrate for boys and delays the age of marriage.

# 4. Data

To identify causal effects, we rely on an RCT methodology, exploiting the initial randomized rollout of the program at the barangay level. We collect the data on individuals who are currently in the age group of 23-24.5 years old, and compare young adults who were residing in treatment barangays during adolescence (and thus were eligible to receive the 4P transfer) to young adults who were residing in control barangays during adolescence (and were not eligible to receive the transfer at any point in their life).

To identify and track these young adults, we use data from _Listahanan_ (2008-09). Using the _Listahanan_ , we were able to identify households who had children in the age-group 12.5-14 years during the time of initial targeting and whether a household was identified to be eligible to receive the 4P transfers. To ensure that we did not pick up any spillovers across siblings within the same household, we limited the sample to families where the child aged 12.5 years or older was the youngest child in the family when the program was expanded to control barangays. We provided the data collection firm with the list of eligible households for each of the 71 barangays included in analysis.

The firm randomly drew a required subsample of households, stratified by gender in each barangay, and validated that the respondent households were indeed residents of the barangay with local officials. If the household was not recognized by the local official, the household was replaced. Among the replacements, 81% are from the same barangay, and 19% are from a different barangay.

After the validation with barangay officials, the field team visited households and administered a short tracking questionnaire with the objective of (i) confirming that at the time of initial 4P rollout there was a child aged 12.5 to 14 in the household; (ii) collecting information on where this child currently resides. If the household could not confirm that one of its members back in 2008 was in the age group and gender as described by Listahanan, the field team would replace this household. In case the child (who is now a young adult and our respondent of interest) continued to reside in his/her original HH, the field team directly approached him/her and sought permission to conduct the primary IE survey (ideally on the same day). In case the child has moved out, the field team collected information on his/her current location from the parents or older members in the original household. Thereafter, the field team tracked the child who had moved out and sought his/her permission to participate in the study. We instructed the survey 8
member is likely to default if other members do likewise, indicating the potential role of group collusion in group-based lending.

The group-based scheme evidently has the added advantage of promoting social capital above and beyond the pre-existing social capital that binds members to form groups. Thus, even after controlling for the initial social capital that helps to sort individuals into groups, we find that there is a positive role of an emerging social capital for women, even if their groups are more homogeneous than men’s groups. This role is measured by the positive effect of an additional quarter of a group’s existence since women’s groups were formed; that is, the higher the number of quarters since women’s groups were formed, the greater the incentive for women to repay. For men, the role of social capital does not work in the same direction, perhaps because men are from diverse groups or because the length of time a group has existed does not necessarily promote higher loan repayment rates or the amounts received by individual group members.

This study’s findings thus confirm the central hypothesis of group-based lending: Groups, either through homogeneity or cross-member effect, matter significantly. At the same time, group-based lending is not a panacea for higher repayment rates because of the potential for collusion, which can negatively affect the repayment behavior of members, including those who are willing to repay, resulting in joint liability. Paradoxically, groupbased lending can improve the rate of individual loan repayment and also reduce it by prompting an individual member to default when other members do so.

Yet this potential pitfall is neither predominant nor universal. The incentive structure we observe for men’s groups does not create such collusion but is rather conducive to loan recovery. Owing to the diversity of men’s groups, a member may repay even if others default, clearly indicating risk pooling across diverse groups of the Grameen Bank’s 28