rafmacalaba/datause-encoder-data huggingface dataset:
initial threshold for arrears is 5 percent of total debt outstanding, and for IMF programs we look only at those for which committments are greater than 50 percent of the country’s IMF quota. While any threshold for defining debt distress episodes would be somewhat arbitrary, it is worth noting that these values are quite high relative to the experience of the typical developing country. Pooling all country-years for all developing countries since 1970, the median value of arrears as a fraction of debt outstanding is 0.4 percent, and we are choosing a threshold that is roughly ten times greater. Similarly pooling all country-year observations, the median value of IMF committments relative to quota is zero, reflecting the fact that less than half the countryyears in the sample indicate the existence of an IMF program including access to nonconcessional IMF facilities. When such programs are in place, the median committment is 52 percent of quota. This means that our threshold identifies only the top half (in terms of committments relative to quota) of all SBA/EFF programs. Note that we do not include access to the Poverty Reduction and Growth Facility (PRGF) of the IMF as a debt distress indicator, since, in many cases, financing from this facility is no longer to meet temprorary payments imbalances but has become a source of long term development finance.<sup>4</sup> Finally, we include access to the Paris Club for debt rescheduling or debt reduction as an indicator of debt distress. The Paris Club itself designates its support as extraordinary financing once all other sources of financing are taken into account within the macroeconomic framework of an IMF-supported program. The rationale is that without such extraordinary financing, countries would not be in a position to service their debt without endangering implementation of the IMF program. In this way -- using the existence of significant arrears, Paris Club arrangements, or resort to non-concessional IMF facilities -- we can be certain that we are identifying severe cases of debt servicing difficulties. In any case, we have verified that our findings are generally robust to reasonable variations in these thresholds.

As a complement to debt distress episodes, we also define non-distress episodes, or “normal times”, to use as a control group in the analysis that follows. We define these

> 4   See a report by the IMF’s Independent Evaluation Office on “The Prolonged Use of IMF Resources.”

9
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33
17 causal interpretation of the Oaxaca-Blinder decomposition estimates, the increase in ITN ownership in high malaria risk zones (HMRZ) of Kenya between 2003 and 2008 decreased infant mortality by 12 deaths per 1000 live births. An estimated 136 live births per 1000 households took place in Kenya during a one year period preceding the DHS 2008. Consequently, the Oaxaca-Blinder estimates imply that 1.64 infant deaths per 1000 households were avoided during that year due to an increased ITN ownership in HMRZ. According to World Malaria Reports (2008, 2010), 15,639,783 ITNs were distributed in Kenya over a five year period mid-2003 through mid-2008. If the annual cost of ITN programs is put into relation to the number of infant lives saved by ITNs per year, the estimated cost per infant life saved is US $1085 is (see Table 8). Similar results were obtained in studies focusing on under 5 mortality, e.g. Yukich et al (2008) estimate costs per death averted at US$ 1174 in Togo, US$ 1222 in Malawi, US$ 1449 in Eritrea, US$ 1745 in Tanzania, and US$ 2926 in Senegal.

These calculations are lower-bound estimates of ITN cost effectiveness, due to the fact that this analysis is restricted to the benefits in terms of lower infant mortality, while ITNs have wider benefits. Most importantly, many child malaria deaths occur after the first year. Additionally, lower transmission rate results in later infections with malaria and ―postponed‖ malaria mortality particularly in intermediate malaria risk zones (Snow and Marsh 2002). Thus, positive effects of ITNs are expected to be larger on under-5 mortality than on infant mortality because the benefits will be found in both HMRZ and IMRZ and a wider age range of children will benefit.

A second reason that the cost effectiveness estimates presented here are lower bounds is that ITNs contribute to lower malaria morbidity and to some extent to lower child comorbidity from other diseases such as diarrhea and tuberculosis (Ashraf et al 2010). Lower morbidity leads generally to less patients and thus lower costs in the health sector. Malaria accounts for 30 percent of outpatient consultations and for 19 percent of inpatient hospital admissions in Kenya (Ministry of Health 2001). In adults, losses in terms of lower productivity and less hours worked are avoided. The Kenyan Ministry of Health (2001) estimates that 170 million working days per year are lost in Kenya due to malaria. For children, lower rates of malaria may increase school attendance and improve cognitive skills are mitigated (Fernando et al 2010).
where _φ_ is the standard normal pdf, and _R_<sup>_ε_</sup> _ji_<sup>istherelevantregionofintegrationforchoice</sup> _j_ . The simplest choice probability is for unconstrained cultivation, which is Pr( _U |_ Θ _, Zi_ ) = ̃ 1 _−_ Φ( ̃ _εAUi/σε_ ), where Φ is the standard normal cdf and the dependence of _εAUi_ on parameters and data is implicit. For autarky,

̃ ̃ where _εAi_ = max _{εLAi,_ ̃ _εCAi,_ ̃ _εPAi}_ represents the highest _εi_ that would induce any kind of water transfer. For the probabilities of arrangements _j_ = _L, C, P_ , the regions of integration are not easy to write out as there are many possible configurations of the relevant thresholds, including cases where _R_<sup>_ε_</sup> _ji_<sup>hastwodisjointsegments.AppendixTableD1enumeratesall38</sup> possible configurations and their associated integration limits.

Now, let _dji_ take a value of 1 when well-owner _i_ chooses water arrangement _j_ = _U, A, L, C, P_ and zero otherwise. The likelihood contribution of borewell _i_ is where _Hj_ = _|∂εji/∂lji|φ_ ( _εji/σε_ ) _/σε_ is the density of irrigated area under arrangement _j_ , the product of the absolute value of the Jacobian of inverse transformation _εji_ ( _lji_ ) and the density of the cost disturbance for the given _j_ .

The first two terms of the likelihood essentially form a tobit model, with _HU_ accounting for the uncensored observations, _lUi < ai_ ,<sup>30</sup> and Pr( _A|_ Θ _, Zi_ ), as given by equation (14), accounting for the censored observations. The third term of the likelihood can be understood using Figure 5, which illustrates two possible configurations for a well-owner selling water on a per-irrigation ( _P_ ) basis ( _VL_ is not relevant for these cases and hence does not appear in the figure). In panel (a), _P_ is optimal only for _εi_ in the left tail. Thus, to observe ̃ ̃ _lP >_ 0, not only must we have _εi < εAi,_ but also _εi < εCPi_ . To account for this additional right truncation, Pr( _P |_ Θ _, Zi_ ) multiplies the truncated density _HP_ ( _lPi_ ) _/_ Φ( ̃ _εAi_ ) in the third likelihood term.<sup>31</sup> In panel (b), _P_ is optimal for moderate _εi_ with _C_ dominating in the left tail. As in the previous scenario, the third term of the likelihood must include Pr( _P |_ Θ _, Zi_ ) to account for this left truncation.<sup>32</sup>

> 30This is, of course, why Pr( _U |_ Θ _, Zi_ ) drops out of the likelihood.

> 31The presence of Pr( _j|_ Θ _, Zi_ ) _/_ Φ( ̃ _εAi_ ) is reminiscent of Cragg’s (1971) hurdle model. In our model, however, there is one error term; i.e., the hurdle is not determined by a second independent error. Note as well that the fixed transactions cost ( _κji_ ) guarantees a discontinuity in the irrigated area schedule.

> 32With only a _single_ transaction type, say _P_ , the third term would collapse to _HP_ ( _lP i_ ) and the likelihood

26
Figure 18: Lower labor market flexibility is associated with a higher rate of self-employment <!-- Start of picture text --> Labor market flexibility and self-employment in EU countries - 2013 Redundancy cost and self-employment in OECD countries - 2013<br>GRC GRC<br>KOR<br>ITA ITA<br>CHEIRL CHL LUX<br>BELSVNESPFINDEUPRTAUT LUXSWECZENLDPOL IRL GBR DNKAUT JPN BELSVNGBRNLDFINFRAAUSISLNORPOLESPSWECZE PRT DEU<br>FRASVK DNK SVK<br>USA CAN<br>coef = -5.2598576, (robust) se = 2.6705206, t = -1.97 EST EST coef = .30458398, (robust) se = .11216335, t = 2.72<br>-1 -.5 0 .5 1 -10 -5 0 5 10 15<br>Labor market flexibility score Redundancy cost<br>20<br>10<br>0<br>-10<br>20<br>10<br>0<br>-10<br><!-- End of picture text --> Source: World Economic Forum (2014); World Bank, World Development Indicators database, 2014 edition. Note: In the left graph, Greece is an outlier but its removal does not affect the negative correlation in the estimation. Relationships are significant at the 10 percent and 5 percent level, respectively, after controlling for GDP per capita.

A high self-employment rate does not necessarily mean that a labor market is inefficient; the assessment needs to be made in combination with other factors. First, a high share of selfemployment may mean a higher level of segmentation in a country to the extent that self-employed workers in a dependent relationship with an employer lack access to the same social protection, salary level or same career opportunities as regular workers.

Second, self-employment is often used as a proxy for informality. Although not all self-employed entrepreneurs operate in the informal sector, the International Labour Organization recognizes the self-employed as part of the informal sector. According to the most recent data, the informal sector in Sweden represents 13.9 percent of the country’s GDP. More than 800,000 Swedish citizens perform at least some kind of work in the informal sector each year. A growing informal sector is associated with lower levels of growth and competitiveness. It is also associated with a loss of tax revenue, which can lead to a greater tax burden on registered labor (World Bank, 2014).

# **4. Conclusion**

The aim of this paper was twofold. First, to provide an analysis of recent trends in Sweden’s labor market regulations to identify strengths and weaknesses in the regulations and assess how these might affect Sweden’s productivity, global competitiveness and the protection and empowerment of its labor force. Second, to analyze the relationship between labor market regulations and labor market outcomes to gain a better understanding of the channels through which regulatory rules may affect these outcomes. The main findings and suggestions of the paper are as follows: 33
We provide in Table 1 several (non-exhaustive) examples of SDG indicators that overlap. For example, indicators 1.5.2 and 11.5.2 both aim to measure the direct disaster economic loss in relation to the global gross domestic product (GDP). Similarly, indicators 11.7.2 and 16.1.3 both track the proportion of persons who are victims of physical or sexual harassment. But standing out even more, the wording in indicator 4.7.1 is identical to that of indicator 12.8.1. The objectives of both these indicators are the same: measuring the extent that global citizen education and education for sustainable development are mainstreamed in the national education policies, curricula, teacher education, and student assessment.

These examples support the notion popular among many development practitioners that the list of SDG indicators can benefit from further refinement. In fact, as adopted by the UN in 2017, the list originally consisted of 244 indicators, which had as many as nine groups of identical indicators. A year later, the UN refined this list to remove duplications, which results in the current list of 232 indicators.<sup>5</sup> Thus our findings suggest that the current list can be refined further, including whittling out overlapping entries.

# _Potentially Conflicting Goals_

The solution to the issue of overlapping indicators discussed above may simply be to comb through the list to reorganize or combine these indicators. In many cases, perhaps we can just use more appropriate wordings. But a more substantive issue is that some goals may conflict with one another.<sup>6</sup> In other words, this situation may require a far more substantial re-thinking of the underlying goal itself. For example, how should we evaluate a country’s performance if this

> 5 - These duplicate indicators are shown on the UN’s official website https://unstats.un.org/sdgs/indicators/indicators <u>list/ .</u>

> 6 Kanbur, Patel, and Stiglitz (2018) point out a related challenge with SDG number 17 that it may be interpreted as a potential catch-all goal consisting of disparate components that do not form a coherent whole.

7
Figure 3. Assumptions of financial stress and commodity prices <!-- Start of picture text --> A. VIX assumptions relative to B. Corporate borrowing spread C. Oil price assumptions<br>baseline for 2021 assumptions relative to baseline<br>for 2021<br>Index Basis points Dollar per barrel Baseline<br>10 80 Downside Upside Downside/upside range<br>60<br>60<br>40<br>40<br>0<br>20<br>20<br>0<br>-10<br>Downside Upside -20 0<br>Q1 Q2 Q3 Q4 2020 2021 2022<br><!-- End of picture text --> Source: Oxford Economics; World Bank. A. Chart shows the combined exogenous and endogenous deviation of the VIX, the Chicago Board Options Exchange’s Volatility Index, from the baseline scenario in 2021.

B. Chart shows simple average of corporate borrowing spreads in the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) and EM7 (China, India, Brazil, Mexico, Russia, Indonesia, and Turkey). Corporate borrowing spread is defined as the difference between the 5-year corporate BBB bond yield and the 10year sovereign bond yield.

C. Oil price is the simple average of Brent, Dubai, and West Texas Intermediate prices.

# Figure 4. Global Growth Scenarios

<!-- Start of picture text --> A. Global growth B. Global trade growth C. Growth in advanced<br>economies<br>Percent Percent Percent<br>6 Baseline Downside/upside range Baseline Downside/upside range 6 Baseline Downside/upside range<br>8<br>4 4<br>4<br>2 2<br>0<br>0 0<br>-4<br>-2 -2<br>-4 -8 -4<br>-6 -12 -6<br>2020 2021 2022 2020 2021 2022 2020 2021 2022<br><!-- End of picture text --> Source: Oxford Economics; World Bank. Note: Aggregate growth rates calculated using GDP weights at 2010 prices and market exchange rates.

15
(28 percent), and groundhog (pesticide) attacks as well as bird attacks (each cited by 19 percent of households in the CFSNS survey).

The inability of the country to produce enough rice and other cereals to feed the population has led to massive imports and has been one of the (many) factors that have led to high levels of food insecurity. The FAO typically describes food insecurity as a situation under which some people lack access to enough food of good quality to meet their nutrition needs in order to be able to lead an active and healthy life. According to the results of the CFSNS survey, most rural households are suffering from some forms of food insecurity: As described in Ministry of Agriculture (2007: 15): “ _Nationally 80% of the rural population is either moderately vulnerable (41%), or highly vulnerable to food insecurity (40%), while only 9% of the rural population is food secure, and 11% are food insecure... Chronic malnutrition rates reach 39% for children under five, and only 32 % of households had access to improved water sources, and other basic services were limited... The most food insecure and highly vulnerable groups [are] involved in palm oil producing and selling (64%) followed by hunters and contract labourers (respectively 61% and 58%). The more food secure and moderately vulnerable groups are among the cash and food crop producers (37%), the petty traders and the employees (44% each)_ ” (see table 2).

Importantly, even cash and/or food crop producers are considered likely to be food insecure (indeed, this group of households is considered as likely as many other groups to be food insecure in table 2), suggesting that food production for auto-consumption often still does not enable many households to meet their food needs.

# **3. Rice production and consumption in the 2007 CWIQ survey**

In this section, the nationally representative 2007 CWIQ survey is used to estimate rice production and consumption, and separate consumption into locally produced rice and imported rice. Table 3 provides summary data on rice consumption and production for autoconsumption, as well as a comparison with a number of other food items commonly consumed in Liberia. The total value of food consumption for the items listed in table 3 accounts for 87 percent of the total food consumption of households (these items were used for estimating a food poverty line in Liberia using the cost of basic needs method, as discussed in Backiny-Yetna et al., 2007). The following comments are worth pointing out:

- Rice (imported and locally produced) is by far the largest food consumption item, accounting for more than a third of the value of total food consumption.  The value of
Table 6 Country Fixed Effects of Pro-Poorness

||Aggreg<br>(1|ateAid<br>3)||Aggreg<br>(1|ateAid<br>3)|
|---|---|---|---|---|---|
|Rank|Country Code|Effect|Rank|CountryCode|Effect|
|1|ETH|1.3805|40|MAR|0.1947|
|2|TJK|1.0473|41|KAZ|0.1927|
|3|NPL|0.9995|42|DZA|0.1919|
|4|PAK|0.9876|43|CHN|0.1747|
|5|BGD|0.9860|44|PHL|0.1558|
|6|MwI|0.9338|45|CAF|0.1174|
|7|BDI|0.9155|46|JOR|0.0819|
|8|MLI|0.8517|47|GEO|0.0634|
|9|GNB|0.8276|48|THA|0.0202|
|10|BFA|0.8187|49|IRN|0.0163|
|||KGZ|0.7772|50|FJI|-0.0426|
|12|LAO|0.7752|51|TUN|-0.0875|
|13|UKR|0.7681|52|MKD|-0.1288|
|14|IDN|0.7468|53|LSO|-0.1417|
|15|UGA|0.6838|54|SVN|-0.2267|
|16|MDG|0.6827|55|HRV|-0.2411|
|17|TZA|0.6822|56|SWZ|-0.2946|
|18|YEM|0.6637|57|JAM|-0.3573|
|19|MDA|0.6461|58|ECU|-0.4513|
|20|VNM|0.6409|59|TUR|-0.4686|
|21|GHA|0.5977|60|MYS|-0.5068|
|22|SRB|0.5957|61|PER|-0.5799|
|23|SEN|0.5527|62|DOM|-0.6054|
|24|MNG|0.5370|63|GTM|-0.6259|
|25|ARM|0.5100|64|PRY|-0.6291|
|26|KEN|0.4870|65|SLV|-0.7485|
|27|LKA|0.4795|66|URY|-0.7829|
|28|EGY|0.4732|67|CRI|-0.7859|
|29|CMR|0.4477|68|MEX|-0.8014|
|30|AZE|0.4225|69|VEN|-0.8024|
|31|ALB|0.3962|70|BLZ|-0.8651|
|32|CIV|0.3667|71|HND|-0.9403|
|33|NGA|0.3602|72|ZAF|-0.9498|
|34|MOZ|0.3346|73|BOL|-1.0209|
|35|MRT|0.3345|74|ARG|-1.1046|
|36|BLR|0.3047|75|SYC|-1.1136|
|37<br>|GMB<br>|0.2694<br>|76<br>|COL<br>|-1.1351<br>|
|38|ZMB|0.2584|71|BRA|-1.1742|
|39|RWA|0.2427|78|PAN|-1.2554|

Notes:See Appendix 2 for the coutry code. Source: Authors' compilation Table 7 Impact ofAid on the Poor by Countries with Pro-Poor Fixed Effects Dependent Variable: Ln(Per Capita Income of the Poorest Quintile)

||Economi|cAid|Social|Aid|Aggrega|te Aid|
|---|---|---|---|---|---|---|
|Pro-PoorCountry Fixed Effects|Positive|Negative|Positive|Negative|Positive|Negative|
||()|(2)|(3)|(4)|(5)|(6)|
|Constant|-3.4638***|—-8.2871***|-3.5905***—|-8.6277***|-4.0518***|—-8.5907***|
||(3.77)|(5.44)|<br>(4.04)|<br>(5.52)|<br>(4.38)|<br>(5.57)|
|Ln(PerCapitaIncome)(realterms)#|1.1242***|=1.6235***|1.1340***|—1.6584***|1.1973***|—1.6536***|
||<br>(8.11)|<br>(8.55)|<br>(8.39)|<br>(8.53)|<br>(8.58)|<br>(8.62)|
|Aid/GDP|-0.0052|-0.1913***|0.0185***|0.0406**|0.0136***|0.0179*|
||(0.46)|(3.03)|(3.46)|(2.44)|(5.32)|(1.96)|
|CrisesDummy|-0.0193|-0.0527|-0.0247|—-0.0730**|-0.0149|—-0.0719**|
||(0.57)|(1.62)|<br>(0.74)|<br>(2.09)|<br>(0.45)|<br>(2.05)|
|No. of Observations|97|65|99|65|98|65|
|R-squared|0.99|0.99|0.99|0.99|0.99|0.99|

Notes: i) Absolute value of t-statistics calculated with White-corrected standard errors is in parentheses. * ** and *** indicate significance at the 10%, 5% and 1% levels, respectively. 1i) Panel two-stage least squares methods is used for these analyses. ili) # denotes variables are instrumented with the value of the previous year. Source: Authors' compilation