rafmacalaba/data-use-mentions-extended huggingface dataset:
increased with the level of education, while they decreased for females: the RoR for primary education was 0.9 percent, it was 2.7 percent for general secondary education, 3.1 percent for vocational education, 7.5 percent for university graduates, and 11.8 percent for those with post-graduate studies. Returns to education for females decreased with higher education levels: the RoR for primary education was 9.9 percent; for general secondary, 8.2 percent; for vocational education, 6.1 percent; and for higher education, 7.4 percent. The study shows that returns to education are higher in the government and public sectors only for early education levels (primary and preparatory), while the RoRs for vocational, university, and higher education levels were superior in the private sector (El Arabi, 2010).

Wahba (1996) studies the determinants of earnings in the Egyptian labor market using the Egyptian Labor Force Sample Survey (LFSS) performed in October 1988. She uses the classical Mincerian model. Her study is consistent with earlier studies in showing an increase in returns to education with education level. She also reports variations among regions, and explains these discrepancies by differences in labor productivity characteristics.

Said (2007) uses the 1998 LFSS, 1998 ELMS, and 2006 ELMPS to study workers’ earnings in Egypt. She ran 18 multivariate regressions of log hourly wage regressed on education levels for three rounds (1988, 1998, and 2006), males and females, sector of employment, and region. Said reports considerable improvement in female wages between 2006 and 1998, due to the concentration of female employment in the government sector, where wages increased by 40 percent between these two periods compared with a 17 percent increase in the private sector. Said reports a 4.7 percent return on vocational education, 7 percent for post-secondary education, and 8.5 percent for university education, without allowing for any heterogeneity.

El Arabi (2010) uses a sample of 862 observations
density, and scattered, discontinuous, and commercial strip development patterns.<sup>56</sup> Sprawl reduces inner‐urban connectivity and typically places higher transportation costs on families living in these areas. However, the effects of sprawl are not just felt in the areas experiencing sprawl, but are also felt by firms and employers in the inner city. Without a competitive workforce in the vicinity, employment centers may experience the ripples of sprawl in the form of increased costs of labor. Similarly, increased infrastructure investments, such as road networks, sewage pipes, and school systems are required to service these sprawling populations, placing a monetary burden on local governments as well.<sup>57</sup> Additionally, non‐monetary effects of sprawl include increased air pollution, loss of natural land, and ecosystem fragmentation.<sup>58</sup> City decentralization is a common effect of sprawl, and leads to longer travel distances and time for households. To measure the degree to which development has diffused across the city, an index

> 54 Pesaresi, M.; Guo Huadong; Blaes, X.; Ehrlich, D.; Ferri, S.; Gueguen, L.; Halkia, M.; Kauffmann, M.; Kemper, T.; Linlin Lu; Marin‐Herrera, M.A.; Ouzounis, G.K.; Scavazzon, M.; Soille, P.; Syrris, V.; Zanchetta, L., "A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results," _Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of_ , vol.6, no.5, pp.2102,2131, Oct. 2013 doi: 10.1109/JSTARS.2013.2271445.

> 55 Azar et al 2010. Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti. International Journal of Remote Sensing. Taylor and Frances. page 5641
on sector i .

Third, rather than focusing on one priority sector alone, the _SIM II_ module allows policy makers to examine how revising a tariff on one or several intermediate input sectors affects costs and effective protection rates across all output sectors. The tool calculates baseline upstream tariffs and effective protection rates for all sectors based on the data uploaded to UTAS. Users can then modify the prevailing tariff on specific input sectors. New upstream tariffs and effective protection rates are subsequently calculated based on the simulated tariff schedule for all output sectors. The difference between baseline and simulated values is then used to assess the overall cost impact of the reform across different sectors.

9
changes, it cannot persuade reluctant reformers. The number and frequency of conditions attached to adjustment loans and standby agreements from international financial institutions have little to do with successful policy reforms. Case studies have shown that effective policy reform generally emerges from domestic political consensus— and conditionality from donors does not help, and sometimes hurts, consensus building. There can be exceptions in the cases of ‘‘stroke-of-the-pen’’ reforms, such as exchange rate adjustments, where only a small number of decision makers need to be convinced of the benefits of a policy change. But even in these cases, policy reforms risk reversal if they do not command broad support.

# **The Importance of Institutions**

The neoliberal “get prices right” view of the world persisted until the early 1990s as World Bank economists struggled to provide useful advice to post-communist transition countries such as Poland, Hungary, the former Soviet Union and China—a group of countries with characteristics that were fundamentally different from those of developing countries. It soon became clear that economists did not have ready-made answer to the problems of transition economies and this helped the evolution of ideas about development. Thankfully, positive progress was made in at least three areas throughout the 1990s and 2000s. First, institutions and the political economy of

> 7  See Adjustment Lending Retrospective, June 15, 2001; Review of World Bank Conditionality, September 2005; Conditionality in Development Policy Lending, November 15, 2007; and Development Policy Lending Retrospective, October 27, 2009.

13
14|June-08 and Mar-11||||||
|||||||_South Asia (2)_||||
||_East Asia (2)_||||Bhutan|2070|lower middle|81|Dec-11|
|Samoa|3190|lower middle|53|Jan-10|Maldives|6530|upper middle|88|Oct-09|
|Solomon Islands|1110|lower middle|21|Feb-09||||||

Source: World Bank and IMF Note: countries in italics are eligible for the HIPC and MDRI initiatives The evaluation of debt management performance in small states presented in this paper is based on DeMPA for the 17 small states listed in Table 1 above and it may not be an accurate reflection of the strengths and weaknesses of debt management performance in other small states. However, the convergence of DeMPA findings in the 17 small states assessed is striking, and indicates broader lessons may apply. The average income, and debt related characteristics, of small states in the sample group is representative of the average for all small states. The average GNI per capita of small states in the sample group is only slightly lower, US$4,329 than that of all small states (US$4,759) and the ratio of domestic public debt to GDP is within the same range (Table 2). As a share of total outstanding external debt of small states, the share owed by those in the sample group has consistently been between 40 and 45 percent throughout the past decade. However, ratio of external public debt to GDP is higher 62 percent (compared to 52 percent) but this is explained by the fact that the sample group includes all five of the most heavily indebted small states, with public debt to GDP levels above 80 percent. A higher share of small states in the sample group had recourse to debt restructuring and this could be taken as an indicator of weaker debt management capacity. However, this is counterbalanced by the
because this algorithm depends on variables endogenous to my outcome of interest, such as the percentage juvenile values in the cluster of sets reported to the regulator.

Specifically, I use the single-linkage clustering algorithm to group sets that occur within 5 nautical miles of each other on the same day between midnight and 3 PM (R Core Team, 2019). I choose this time period because closures that begin at midnight (91% of closures during my study period) must be announced by 3 PM (9 hours in advance). The remaining 9% of closures begin at 6 AM and must be announced by 6 PM of the previous day. I use a 5 nautical mile threshold in the single-linkage clustering algorithm because the regulator’s second-stage algorithm rounds the boundaries of rectangular closures to the nearest 5 nautical mile interval (Instituto Humboldt and SNP, 2017). Then for each cluster containing more than three sets, I draw a rectangle to cover its convex hull (the smallest convex polygon that encloses the cluster), rounded up to the nearest 5 nautical mile interval. I drop all potential closures that are smaller than the smallest closure declared by the regulator that season.

As an illustrative example, Figure 3 displays sets from the electronic logbook data in one region of the fishery between midnight and 3 pm on April 28, 2019. The single-linkage clustering algorithm creates two clusters from these sets (Figure 3a). Figure 3b displays the potential closures that result from these clusters.

I assume all potential closures last for three days, which is the modal length of closures declared by the regulator (the regulator can also declare closures that last four or five days). Since a closure cannot be declared in the same place and time as an already-existing closure, I loop forward in time and subtract areas of potential closures that overlap with alreadyexisting potential closures. I drop potential closures that have become non-convex or smaller than that season’s smallest closure after this procedure.

My potential closures algorithm generates 973 potential closures in total, compared
# **Introduction**

The soundness of the banking sector depends on prudential lending practices and on the soundness of the borrowers. Systemic shocks to the borrowers’ ability to repay their loans will be transmitted to banks through corporate defaults, thus affecting banks’ soundness. Traditionally, bank stress testing does not include corporate stress testing. However, including corporate vulnerability in the evaluation of bank stability could provide a more precise measure of vulnerability in banking and allow policymakers to take necessary steps to prevent bank crises.

This paper offers a simple methodology for evaluation of the soundness and vulnerabilities in corporate sector, which can then be linked to bank stress testing. The paper uses data from Jordan to demonstrate the methodology.

There are two reasons to perform such corporate vulnerability assessment: First, using corporate data is likely to strengthen bank stress testing exercises since it provides an independent way to assess the assumptions about the impact of shocks. Second, Jordan is a particularly suitable country for this exercise because there is an active stock exchange with data on over 150 listed non-financial companies publicly available for 2000-2006.

The paper will proceed in three sections. First section presents benchmarking of corporate vulnerability in Jordan against comparable countries using Worldscope data. Second section contains detailed analysis of corporate vulnerability using firm-level data on publicly listed firms (available from ASE). Specifically, measures of vulnerability will include leverage, performance, growth, and interest coverage (ability to repay the debt). These measures will be broken down by sector, firm size and over time. Third section contains corporate stress testing, investigating the impact of interest rate and earnings shocks on corporate vulnerability measures.

# **2. Cross-Country Benchmarking of Corporate Vulnerability**

This section compares key vulnerability ratios in Jordan and four comparator countries with available data: Egypt, Israel, Morocco and Turkey. The analysis relies on publicly available data through the Worldscope database, which contains data on largest, most liquid public firms for which there is foreign investor interest.

2
indistinguishable. The price data from DAM however are available for one additional year (2008) compared with the TCB data. Moreover DAM reports price data for a wider range of commodities compared to TCB. We thus use the DAM data for our empirical analysis. The daily international price data of wheat are derived from the data stream of Chicago Board of Trading.<sup>21</sup> Crude palm oil price data are taken from the Malaysian Palm oil Board. Lentil import unit values are taken from the National Bureau of Revenue daily import data. Our sample extends from January 24, 2008 to October 4, 2012. There are however some data gaps due to lack of price data during weekends and holidays as well as some missing data in the DAM original data set. Our final sample for palm oil and wheat includes 966 days spread over 57 months.

To provide a feel of the data used in the analysis, Table A.1 in the online appendix reports summary statistics for the prices and the margins for palm oil and wheat during pre and postreform periods. For palm oil, the world-wholesale margin, the focus of our analysis, has increased in the post-reform period. In contrast, the margin has declined for wheat marketing in the postreform period. In the following, we present the estimates of the policy effect on the marketing margin from formal econometric analysis.

# **(7) The Effects of the Reform: Empirical Evidence**

# **(7.1) Estimates from Before-After Comparison**

The estimates from a before-after comparison of the world-wholesale marketing margin are reported in Table 1.<sup>22</sup> We define the trading margin using alternative measures of crude oil costs. We report two sets of results, using the same-period (top panel) and two week lagged (bottom panel) values of world crude price as the relevant costs. The choice of two weeks lag is motivated by the fact that it takes about two weeks to transport crude oil from
educational attainment causally impacts health, parenting, and other aspects of life, producing both private and social benefits (Oreopoulos and Salvanes 2011). Across several countries where studies have been completed, perpetrators of crime, mostly men, have lower levels of completed education than the general population, and in a cross-country panel analysis, the rate of secondary completion in particular is strongly negatively correlated to violent crime rates (Chioda 2013). Chioda (2013) reviews the evidence from well-identified studies of the effects of increased schooling on crime, showing that measures that increase time spent in school, years of schooling, and the quality of schooling can all reduce crime in the short and long run.

# **3. Exploration of the causes of dropout before completing upper secondary school**

As shown in Table 1, countries in Central America differ in the segment of secondary where dropout takes place. Therefore, as discussed in the conceptual framework above, the reasons for dropout are likely to vary from country to country, as well as along the schooling trajectory. A relevant source of information for understanding the causes of dropout is found in the same household surveys used so far, as most of them directly ask youth who have left the education system to register the main reasons why they decided to leave school.<sup>15</sup> Figure 13 summarizes the self-reported causes for individuals of secondary school attendance age for the five Central American countries for which household surveys report this indicator. We have grouped the answers into five categories, comprising _economic_ factors (related to having to work, insufficient family or personal income for investing in education, not enough resources for travelling to school or to purchase materials, etc., and poverty), _personal_ factors (sickness, having to perform household responsibilities, pregnancy, marriage, etc.), _not interested_ in studying (which includes categories such as “dislikes school”, “considers what schools teach to be irrelevant”, “thinks that what is learned in school is not
with a liberalized grain market. Kenya‘s government has retained some control over maize grain markets, but has largely liberalized fertilizer markets and to some extent seed markets. Zambia‘s and Malawi‘s governments exert strong control over both input and maize grain markets.

# _Seed Policies_

The burgeoning demand for maize grain in Sub-Saharan Africa would suggest a healthy farmer demand for certified seed, but even though maize seed markets may be better developed than are markets for most other crops, the practice of farmer seed-saving remains common. For example, although hybrids have been widely adopted in Zambia, survey data suggest that during the 2007/8 maize growing season, 59 percent of maize growers use non-traded or recycled seed (also see Chapter 9 for Kenya and Uganda).

The central role of the seed industry has been repeatedly emphasized in policy analyses of Eastern and Southern Africa, and much progress has been made in developing the private seed sector in this region based on hybrid seed. In 2009, seed companies, including a few public companies, accounted for 98 percent of the market. More than half were private national companies, and about one fifth each were multinational and regional private companies (Langyintuo et al., 2009).<sup>6</sup> Langyintuo et al. (2009) concluded that the major bottlenecks in the seed industry of Eastern and Southern Africa were lack of awareness of the availability and value

> 6 Nongovernmental organizations and national research organizations accounted for a scant 4 percent of all seed marketed in the region.

13
If the shock is temporary and the firm is not credit constrained, then this probability is reduced to only 2%. For firms that are credit constrained (column 4), we do not find a significant different response to permanent and temporary shocks.

_Welfare costs of incomplete input smoothing due to credit constraints_ In this analysis, we find that firms that are credit constrained are smoothing less than optimally. And the CRBS 2009 survey indicated that about half the firms expected the negative demand shock to be temporary, while 60% of these firms also reported that the availability and cost of finance had changed and that they had become more credit constrained.

What is the welfare cost of such incomplete input smoothing due to a credit constraint? This is a policy relevant question because improving access to finance may be a feasible policy option for economic policy-makers when firms are faced with a large negative demand shock.

In case the shock is temporary and the firm is credit constrained, then total costs are increased by 2 T,cr T T,cr T 2 T,cr T w(x2 −x2 ) + c[�x̅ −x2 � −(x̅ −x2 ) ], where x2 ≤x2 is the choice of input by a firm that is credit constrained. The change in total cost (as a proportion of total adjustment costs for a non-credit constrained firm) can be written as where ∆x2T,cr = x̅ −x2T,cr and ∆x2T = x̅ −x2T. Note that ∆x2T,cr ≥∆x2T and therefore the expression shows that a firm which is credit constrained spends less on input costs but more on adjustment costs. If we assume that the non-credit constrained firm is smoothing inputs, and because w = 2∆x2T (see equation c 3), we can rewrite equation (6) also as 27
goods supplied by foreign firms to Chinese companies when the equipment was needed to fulfill processing contracts.<sup>35</sup> On the eve of accession (2000), export processing trade accounted for 41 percent of imports and 55 percent of exports in China. The remaining 45 percent of exports, called ordinary exports, were produced mainly with domestic inputs and only a small portion of duty–paid imported materials.<sup>36</sup> According to the Customs authorities, in 2000, 60 percent of imports entered China duty-free, out of which 41 percentage points were imports used for export processing, 13 percentage points were capital goods, and 6 percentage points were goods that fell in special categories.<sup>37</sup>

# _4.1.1 Benefits associated with duty drawbacks in China_

Duty drawbacks in China reduced the anti-export bias of the old planned economic system and improved the competitiveness and efficiency of the economy by allowing export producers to import at international prices. In the absence of duty exemption schemes, exporters in China would have faced negative effective rates of protection (Figure 1 in Ianchovichina 2004). Duty drawbacks reduce the negative effective rates of protection (ERPs) for export competing firms, allowing many export producers to operate at world prices and compete more effectively in world markets. Duty drawbacks reduce the dispersion in ERPs (Table 2), and therefore increase economic efficiency (Bach and Martin, 2001).

> 35 China’s State Council decided to eliminate these exemptions as of April 1, 1996. However, this deadline was not strictly enforced and eventually the exemption program was formally re-established (Lardy, 2002).

> 36 According to version 4 GTAP database (McDougall _et al_ ., 1998), in 1995 China’s firms exported on average 10 percent of their output and 14 percent of Chinese imports were for final consumption. According to China’s Customs 40 percent of imports were ordinary imports that were not duty exempt. Thus, approximately 26
|||Table 4C:|Regression|results from|the full samp|le of153 coun|tries and al|l years 1988-|2015||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||)<br>Logo|Q)<br>ftotal loans (|8)<br>millions of|@)<br>2010 $)|6)<br>Logo|6<br>fHD loans (|0<br>millions of2|8<br>010 $)|9)<br>LogofH|1)<br>D sectorloa|dy<br>ns (millions|of2010 $)|
|IBRD country|-0.124|-1.053***|= -0.508*|= -0.651***|-0.270|-0.916***|-0.774***<br>|= -0.868***|-0.297|-0.933%**|-0.587***|-0.691***|
|(1=yes)|(0.311)|(0.239)|(0.275)|(0.249)|(0.172)|(0.154)|(0.239)|(0.245)|(0.184)|(0.164)|(0.212)|(0.219)|
|Logpopulation||0.767***||1.324*||0.437***||0.764||0
fluctuations helps reveal long-term trends of variables, such as GDP series. The availability of a long time series of the Argentinian GDP (1961-2022) that far exceeds the period analyzed allows for a more precise estimation of the cycle.<sup>23</sup> This study explicitly addresses the direct impact of commodity export taxes on the structural budget balance. Export taxes on food products are tied to global commodity prices. Accordingly, when these prices soar, the governments’ tax revenues increase, immediately improving their fiscal position. It is worth noting that second round effects from a spike in commodities prices boosting the agricultural production and exports, are not being captured by this approach, as this exercise focuses exclusively on direct tax revenue stemming from export duties. These sources of taxation are not commonly used but are particularly relevant in a few commodities-reliant developing countries. Thus, several studies recalculate the receipts from these sources of taxation by applying a long-term price adjustment related to the commodities exported by the country under analysis (Marcel et al., 2001; Alberola & Montero, 2008; Basco et al., 2007; Daude, 2010). This study considers export duties generated by commodities exports, thus leaving aside export duties applied to non-commodities products.

From (5) we can account for the impact of commodities prices through the receipts obtained from export duties. We define structural revenues related to commodities ( Re<sup>s</sup> ) from the following equation where Re are the export duties obtained strictly from commodity products, p<sup>l</sup> is the long-run price of commodities and p is the spot price of commodities. Price elasticity γ is considered to be unitary following the approach of Marcel et al. (2001), Zettelmeyer & Hollar (2008), and Daude et al. (2010).

Argentina introduced export duties after the 2002 crisis to capitalize on the devaluation of the currency among other considerations. This source of taxation was typically used in the past as an emergency