rafmacalaba/datause-encoder-data huggingface dataset:
perceptions, through two channels. On the one hand, it reduced future growth expectations, and on the other it made further fiscal adjustment more difficult and painful as the ratio of revenues to GDP collapsed. In this context, further tax hikes (like the "impuestazo" in 2000) or expenditure cuts (like those undertaken during the second half of 2001) aggravated the recession and the ensuing social and political tensions.

**Table 4.4. Fiscal Sustainability and the Exchange Rate**

||**DebtOutput**<br>**Ratio**|**DebtOutput**<br>**Ratio**<br>**Adjusted**<br>**forRER**<br>**Misalignment**|**Consolidated**<br>**Gov.Primary**<br>**Balance**|**Sustainable**<br>**Balance**<br>**(av.**growthrate<br>**basedonlast3 **<br>yearobservations)|**Sustainable**<br>**Balance**<br>**Adjusted**<br>**forRER**<br>**Misalignment**|
|---|---|---|---|---|---|
||||Percentageof|Percentageof|Percentageof|
||||GDP|GDP|GDP|
|**1991**|32.3%|28.0%|-0.4|2.7|2.1|
|**1992**|26.1%|23.9%|1.4|0.1|0.1|
|1993|28.7%|27.7%|1.2|n.s.p.|n.s.p.|
|**1994**|30.9%|29.1%|-0.1|n.s.p.|n.s.p.|
|**1995**|34.8%|31.6%|-1.0|0.8|0.8|
|1996|36.6%|34.9%|-1.3|0.9|0.9|
|**1997**|38.1%|39.4%|0.2|0.9|0.9|
|1998|40.9%|46.2%|0.6|0.2|0.2|
|**1999**|47.6%|63.2%|-1.6|1.7|1.9|
|2000|50.9%|70.6%|0.3|3.9|5.1|
|**2001**|62.2%|95.0%|-1.4|6.0|8.4|

**Note:**

n.s.p. means no sustainability problem.

Even more, the observed adjustment in the structural primary balance was clearly insufficient if we take irnto account both the direct and indirect effects of exchange rate overvaluation since 1997 on the balance sheet of the government. The overvaluation implied that measures of sustainability based on the observed ratio of public debt to GDP understated by a considerable margin the public sector's difficulties, as most public debt was denominated in dollars while government assets (in particular its capacity to tax) were not. Thus, a real depreciation restoring real exchange rate equilibrium would have raised public debt ratios by a large amount - up to 20-30 percentage points of GDP in 2000-2001, as shown in Table 4.4. It is important to note that this would have occurred irrespective of whether the real depreciation was achieved under Convertibility through nominal deflation or through a nominal devaluation and thus a collapse of the Currency Board.3 In either case, the real depreciation would have eventually revealed the reduced capacity of the government to pay back its debt. Table 4.4 shows that once this is factored into the analysis, by 2001 government solvency would have required an additional primary surplus of about 2% of GDP annually. The peg actually hid from public view the increasing precariousness of the fiscal situation, and thus made it more difficult to elicit political support for an adjustment while there was still time for an orderly correction.

> **35** This is underscored by Rotbini (2001).

37
# **Table 7: Growth and Incomes of the Poor**

|||(Dependen|t Variabl|eisln(Per|Capita|Incomein|theB|ottomQuin|tile))||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Tra<br>Volu<br>Coef|de<br>mes<br>Std-Err|Govern<br>Consumpt<br>Coef|ment<br>ion/GDP<br>StdErr|log(1+ln<br>Ra<br>Coef|flation<br>te)<br>Std,Er|Fina<br>Develo<br>Coef|ncial<br>pment<br>StdErr|Rule <br>Ind<br>Coef|ofLaw<br>ex<br>StdErr|AllGr<br>Varia<br>Coef|owth<br>bles<br>Std.Err|
|ln(PerCapitaGDP)|**1.094**|**0.108**|**1.050**|**0.085**|**1.020**|**0.089*****|**0.995**|**0.119**|**0.914**|**0.105**|**1.140**|**0.100**|
|(Exports+lmports)/GDP|-0.039|0.088|||||||||0.023|0.056|
|GovemmentConsumption<br>/GDP|||-0.571|0.419|||||||-0.746|0.386|
|ln(1+Inflation)|||||-0.136|0.103|||||-0.163|0.107|
|CommercialBankAssets<br>/TotalBankAssets|||||||0.032|0.257|||-0.209|0.172|
|RuleofLaw|||||||||0.084|0.069|-0.032|0.060|
|P-Ho:a1=1<br>|**0.386**||**0.555**||**0.825**||**0.968**||**0.412**||**0.164**||
|P-OID<br>|0.257||0.168||0.159||0.350||0.279||0.393||
|T-NOSC<br>|-0.751||-0.506||-0.261||-0.698||-0.945||-0.762||
|#Observations|223||237||253||232||268||189||

> Source: Dollar and Kraay (2001), Table 5.

Notes: All regressions include regional dummies. The row labelled P-Ho: al=l reports the p-value associated with the test of the null hypothesis that a,x=l. The row labelled P-OID reports the P-value associated with the test of overidentifying restrictions. The row labelled T-NOSC reports the t-statistic for the test of no second-order serial correlation in the differened residuals. Standard errors are corrected for heteroskedasticity and for the first-order autocorrelation induced by first differencing using a standard Newey -West procedure.

42
**Table 8: Probability of being a poor cell in a non-poor household (household fixed effects).**

|VARIABLES|(1)<br>_All|(2)<br>_80% richest<br>hh – non-poor|(3)<br>20% poorest<br>hh – non-poor|
|---|---|---|---|
|Cell head is a woman|1.860*<br>(0.658)|2.475**<br>(1.035)|1.226<br>(0.915)|
|Age of cell head|1.032**|1.021|1.061*|
||(0.0147)|(0.0172)|(0.0342)|
|Cell size|1.174*|1.135|1.508*|
||(0.105)|(0.116)|(0.342)|
|Nb of children 0 - 4 y.o. in cell|1.640**|1.536*|1.462|
||(0.325)|(0.380)|(0.566)|
|Nb of children 5 - 14 y.o. in cell|0.981|0.967|0.826|
||(0.144)|(0.165)|(0.279)|
|Cell head is a child of hh head|2.114|1.484|6.139|
||(1.134)|(1.008)|(7.006)|
|Cell head is parent or grand-parent of hh head|0.964|1.079|1.051|
||(0.726)|(0.893)|(1.885)|
|Cell head is a sibling of hh head|2.341|2.017|2.229|
||(1.323)|(1.262)|(2.884)|
|Cell head other family link to hh head|2.830**|2.382*|3.505|
||(1.231)|(1.220)|(3.475)|
|Cell head is the spouse of hh head|2.134*|1.656|4.328*|
||(0.843)|(0.777)|(3.619)|
|Cell head is literate|0.864|0.939|0.857|
||(0.302)|(0.396)|(0.545)|
|Cell head has no formal schooling|1.447<br>(0.548)|1.114<br>(0.498)|5.132*<br>(4.693)|
|Observations|501|338|163|
|Pseudo-R2|0.23|0.22|0.34|

Source: “Pauvreté et Structure Familiale” (PSF) survey, authors’ calculations.

Note: Conditional logit estimates (household fixed effects) of the probability of being a poor cell in a non-poor household. Odds ratio reported. Coefficient standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Cell head’s relation to the household head reference category: cell head is the household head.

44
- 21 -

**<u>Table 4. Probit regression with different specifications</u>**

||Probit||Probit||Probit||Probit||
|---|---|---|---|---|---|---|---|---|
||Health a|ccess (_Y_)|Health a|ccess (_Y_|)<br>Health a|ccess (_Y_)|Health a|ccess (_Y_)|
||Coef.|Std. Err.|Coef.|Std. Err|.<br>Coef.|Std. Err.|Coef.|Std. Err.|
|Travel time to HF|-0.590|(0.153) ***|||||-0.584|(0.153) ***|
|Transport cost|||-0.239|(0.059)|***||||
|Road distance|||||-0.012|(0.004) ***|||
|Bike/Motorcycle|0.105|(0.042) **|0.098|(0.042)|**<br>0.098|(0.042) **|0.109|(0.042) ***|
|Car|-0.027|(0.086)|-0.028|(0.086)|<br>-0.013|(0.086)|-0.033|(0.089)|
|Type of HF:|||||||||
|Urban health center|0.266|(0.081) ***|0.262|(0.081)|***<br>0.260|(0.080) ***|0.257|(0.082) ***|
|Hospital|0.305|(0.098) ***|0.303|(0.098)|***<br>0.309|(0.098) ***|0.295|(0.099) ***|
|Health post|0.027|(0.082)|0.025|(0.082)|<br>0.071|(0.081)|0.021|(0.083)|
|Remoteness of HF|-0.118|(0.027) ***|-0.115|(0.027)|***<br>-0.125|(0.027) ***|-0.116|(0.028) ***|
|Age|-0.003|(0.001) ***|-0.003|(0.001)|***<br>-0.003|(0.001) ***|-0.003|(0.001) ***|
|Female|0.040|(0.036)|0.038|(0.035)|<br>0.039|(0.035)|0.040|(0.036)|
|HH size|-0.002|(0.008)|-0.003|(0.008)|<br>-0.002|(0.008)|-0.002|(0.009)|
|Female HH head|0.008|(0.040)|0.003|(0.040)|<br>0.003|(0.040)|0.006|(0.040)|
|Age of HH head|-0.001|(0.001)|0.000|(0.001)|<br>0.000|(0.001)|-0.001|(0.001)|
|No of bedrooms|-0.023|(0.021)|-0.020|(0.021)|<br>-0.023|(0.021)|-0.023|(0.021)|
|Wealth index|0.116|(0.021) ***|0.114|(0.021)|***<br>0.118|(0.021) ***|||
|Wealth index: 2nd quantile|||||||0.050|(0.058)|
|3rd quantile|||||||0.205|(0.062) ***|
|4th quantile|||||||0.334|(0.071) ***|
|5th quantile|||||||0.455|(0.094) ***|
|t=2018|0.155|(0.051) ***|0.151|(0.050)|***<br>0.133|(0.050) ***|0.155|(0.051) ***|
|constant|0.305|(0.216)|0.301|(0.215)|<br>0.279|(0.213)|0.436|(0.212)**|
|No. of obs.|5766||5792||5819||5766||
|Wald chi|657.4||656.99||659.63||658.56||
|No. of district dummies|113||113||113||113||

Note: The dependent variable is _Y_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

To interpret the results better, the marginal effects are computed by using the delta method ( **Table 5** ). By reducing the travel time required to visit a health facility by 1 percent, the probability of visiting a health facility could be increased by about 2.9 percent. Although the costs of improving transport connectivity vary depending on circumstances, this estimated increase in health care access could bring significant socioeconomic benefits to the country. Recall that Mozambique is estimated to spend nearly US$300 million or 2 percent of GDP for malaria treatment every year.
Figure 7: School attendance and Secondary School or Higher Education Completion <!-- Start of picture text --> Secondary School Completion School attendance<br>.4 1<br>2008<br>2014<br>95% CI<br>.8<br>.3<br>.6<br>.2<br>.4<br>.1<br>.2<br>2008<br>2014<br>95% CI<br>0 0<br>5 10 15 17 20 24 25 5 10 15 17 20 24 25<br>Age Age<br>Proportion attending school<br>Proportion with secondary or higher education<br><!-- End of picture text --> _Source_ : Own calculations using the 2008 and 2014 Lesotho Demographic Health Survey data. _Notes_ : Household survey weights have been used to get the age-specific proportions.

increased. Therefore, without an increase in its capacity, the university may have admitted the best students from a larger pool of qualifying students compared to the pre-FPE period. This would overstate the real impact of the FPE program on achievement. Second, it could be that the pre- and post-FPE cohorts that got admitted into the university differ by household wealth. Specifically, the post-FPE cohort could be poorer than the pre-FPE cohort, which would likely attenuate the FPE program effect. Therefore, I use a nationally representative data from the 2008 (pre-FPE period) and 2014 (post-FPE period) Lesotho Demographic Health Surveys (LDHS) in order to make sense of who actually got into university pre- and post-FPE.

The left panel of figure 7 shows the proportion of individuals who have completed secondary or higher education by age, pre- and post-FPE, and the right panel of the figure shows the enrollment rates by age, pre- and post-FPE. In 2014, the first age-appropriate FPE cohort to enter the university was aged 20 years old. We can see from the left panel of figure 7 that, pre- and postFPE, the age-specific probability of completing secondary or higher education is statistically equal for those aged 21 and below. The right panel of the figure further shows that the enrollment rates of those aged between 14 and 24 years are statistically equal pre- and post-FPE. Taken together, this implies that the pool of potential university applicants did not increase significantly post-FPE. It is, therefore, less likely that the university could have admitted students from the very top end of the achievement distribution of all qualifying students post-FPE compared to those admitted 25
measures, or highly correlated multidimensional measures. Cognitive ability is measured using a test inspired by Raven’s matrices (Raven, 1936). For each of six pictures, individuals are given a score of 1 for a correct response and 0 for an incorrect response, then divided by a total of six questions such that the final average score falls between 0 and 1.

The social desirability index (SDI) utilized is based on the 8-item impression management dimension of a short form of the Balanced Inventory of Desirable Responding (BIDR; Hart et al. (2015)) with a 5-point Likert response scale. The items used to assess SDI generally include a series of actions that are deemed socially desirable but uncommon, or socially undesirable but common. Here, the team excluded the dimension of the longer-form BIDR that is focused on self-deceptive enhancement, primarily due to time limitations. An average of the responses to the eight included items is taken, resulting in a score falling between 1 and 5.

Finally, a variable on equitable beliefs is constructed based on the reversed Likert response to the statement _“By nature, men are better at problem solving and decision making than women.”_ . The question focused on this particular skill since gender-biased beliefs about this skill were a common finding in qualitative data collection during the SES measure — development. The score for each individual is an integer between 1 and 5 and by its reverse nature, a higher score represents a more gender-equitable belief.

# **2.4 Sample description**

The sample was selected using a community sampling frame of streets (a local administrative unit) which met the following criteria: they had at least 120 eligible young men and 120 eligible young women; community leaders confirmed interest in the program; and there was an existing venue suitable for hosting a training. 40 communities were randomly selected by Stata from this list, stratified by city. Within selected communities, we conducted a — listing to create a sampling frame of all eligible individuals defined as those age 16 to 27, not in full-time education or attending boarding school, and not in full time formal salaried 10
privatization took place, the bigger the sales growth and current liabilities gap currently is. This result implies that instead of becoming more similar to other private companies, former SOEs seem to have followed a different path after privatization.

**Exhibit 2.23: Statistical differences between former SOEs vs. non-SOEs** _-_ _<u>Quantile regressions for period 2003 2007, corrected for firm size and industry</u>_

||**Return**<br>**Equity**|**on**<br>**, %**|**Retur**<br>**Asset**|**n on**<br>**s, %**|**Real annual**<br>**sales growth, %**<br> <br>|**Operating prof**<br>**margin, %**|**it**|
|---|---|---|---|---|---|---|---|
|**Difference vs. private**|0.3||-1.0||-7.8<br>|0.3||
|**Statistical difference?**|No||No||Yes<br>|No||
||**Retained**<br>**earnings to**<br>**equity, %**|**EBIT t**<br>**expens**|**o interest**<br>**es, x**|<br>**Total de**<br>**common**<br>**%**|**bt to**<br>**equity,**<br>**Current to total**<br>**liabilities, %**|**Total liabilities**<br>**to Equity, %**|**Paid-in to total**<br>**capital, %**|
|**Difference vs. private**|-3.8|1.8||-9.2|11.1|11.0|-2.0|
|**Statistical difference?**|No|No||No|Yes|No|No|

Source: Coface; World Bank staff analysis _Note: This table is the result of company-level median regression analyses of 234 Egyptian companies where differences in industry and company size are accounted for._ 20
10 While payment of severance and long-service leave after a job separation is mandated by legislation in Indonesia, information from survey data suggests that there is a high rate of non-compliance with the regulations. Approximately one-third of legally eligible wage-and-salary employees receive a severance payment after a job separation occurs; depending on the survey, the average ratio of the severance payment received to the legally entitled amount is either slightly over one-third (IFLS, 2007) or just under one-third (Sakernas, 2008).

There are two aspects of non-compliance. The first aspect is the proportion of eligible formal sector employees who receive a payment. The second aspect is the proportion of the entitlement amount that is actually received. The receipt of severance pay depends on two factors: the proportion of job separators who are eligible (as described in the previous section) and the proportion of those eligible who actually receive a payment after a job separation occurs. Both factors determine the ratio of actual severance payment to the legally entitled amount. The present discussion emphasizes this ratio.

In both surveys, the proportion of eligible employees who received severance pay increases with age but is essentially the same for individuals with differing levels of formal education. In the IFLS (2007) survey, there is no difference between women and men in terms of severance pay receipt. In the Sakernas (2008) survey, however, eligible men are about 39 percent more likely to receive severance than eligible women.

There is variation in the share of eligible persons who received severance pay by industry in both surveys, but there is no apparent pattern. There is a strong systematic relationship between the share of eligible employees who received severance pay and tenure: the share increases with the years in continuous employment with the same firm. Similarly, there is a strong systematic association between the share of eligible employees who received severance pay and the size of the firm: the proportion steadily increases with firm size. About one-quarter of eligible wage-and-salary workers receive severance pay if they are employed by a government agency. Eligible wage-and-salary workers employed by foreign or transnational corporations are the most likely to receive severance pay, while eligible workers employed by state-owned enterprises or domestic private firms are equally likely to receive severance pay. There is more variation in
21

# **_Financial systems are multidimensional._**

One basic, yet important, observation highlighted by the Global Financial Development Database is that the four financial system characteristics are far from closely correlated across countries (Figure 4). This underscores the point that each dimension captures a very different, separate facet of financial systems. In other words, looking only at financial depth would not be sufficient. Similarly, focusing only on financial stability or on access or on efficiency would not suffice. The same applies both to financial institutions and to financial markets.

It is therefore necessary to examine not only financial depth, but also access, efficiency, and stability, to arrive at a relatively comprehensive picture of financial systems. Attempts to run a more rigorous ―horse race‖ among the indicators from the four dimensions tend to end a tie, that is, none of the indicators is clearly superior to the others in terms of its ability to explain longterm growth or poverty reduction.

The analysis suggests that having a deep but unstable financial system is bound to create problems, as is for instance having a system that is stable but small, also having an efficient system that does not provide adequate access is also suboptimal, and so on. This is consistent with the findings of the previous literature. Repeated or protracted systemic crises are clearly detrimental to broader economic development. However, countries that have experienced occasional financial crises have, on average, grown faster than countries with stable financial conditions (Ranciere, Tornell, and Westermann 2008).

# **_Important differences remain across regions and income groups._**

A comparison at the regional level shows major differences in financial systems among the key regions (Table 3). The results are by and large in line as one could expect, with Sub-Saharan Africa scoring the lowest on average on most of the dimensions, and high income countries scoring the highest on most dimensions. A remarkable number is the relatively low score of Middle East and North Africa on access to finance (Table 3, upper panel). This resonates with the complaints heard during the unrest in the region in 2011.

Much of the differences among regions are correlated with differences in income levels. Countries that have lower income tend to also show lower degrees of financial development as approximated by the 4x2 framework (Table 3, lower panel).

# **_Disparities in financial systems across countries are large._**

Behind these regional and peer group averages are vast differences among individual countries, and in some cases also major differences among different parts of each country financial sector. The data from the Global Financial Development Database demonstrate rather strikingly the large differences in financial systems around the globe. For example, the largest financial system in the sample is more than 34,500 times the smallest one. Even if the financial systems are re-
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