rafmacalaba/datause-encoder-data huggingface dataset:
teacher characteristics: the decline in the proportion of certified teachers (from 94% to 82%) and the increase in the proportion of schools giving pay incentives to teachers (from 12% to 21%).

Further to the reported increase in the TIMSS mathematics and science scores from 2003 to 2007 (by 12% and 19% respectively), graphs A1 and A2 show the over-time changes in Kernel density functions. For mathematics, the increase in scores is fairly similar across the score distribution; for science, on the other hand, over-time the variance of the distribution decreased and the increase in scores is smaller at higher points in the distribution of scores.

The above changes in distributions (especially for science) suggest that over-time changes in the degree of inequality in student performance have taken place. Table 2 gives measures of student performance inequality over-time. All estimates are consistent with a decrease in performance inequality for both mathematics and science, but much more for science. Based on the Gini (as well as the relative mean deviation and the coefficient of variation), inequality declined by about 8% for mathematics and by more than 22% for science. The decline in inequality is substantially higher when based on Theil’s entropy measure. In the case of science, percentile scores suggest that the decline was more pronounced at the lower part of the distribution of scores.

Table 2: Inequality measures for Mathematics and Science scores and their over-time changes

||**200**|**3**|**2007 **||**Change**|**(%)**|
|---|---|---|---|---|---|---|
|Statistic|**Mathemati**<br>**cs**|**Science**|**Mathemati**<br>**cs**|**Science**|**Mathemati**<br>**cs**|**Science**|
|Relative mean deviation|0.115|0.173|0.105|0.131|-8.7|-24.3|
|Coefficient of variation|0.288|0.419|0.264|0.326|-8.3|-22.2|
|Gini coefficient|0.162|0.239|0.149|0.185|-8.0|-22.6|
|Theil entropy measure|0.042|0.094|0.035|0.056|-16.6|-40.4|
|p90/p10|2.14|3.50|2.04|2.58|-4.7|-26.3|
|p90/p50|1.42|1.60|1.38|1.47|-2.8|-8.1|
|p75/p25|1.50|1.95|1.43|1.60|-4.7|-17.9|
|p50/p10|1.51|2.19|1.47|1.75|-2.6|-20.1|

Source: author’s calculations.

Prior to model estimation and derivation of results, we deal with the problem of missing information. Several variables have missing values, as some students and school principals did not answer parts of the questionnaires. The extent of missing values in the variables used in the model ranges from 0 to about 10%. Data for missing responses were imputed using the method of relating observations in the original data to a set of “fundamental” variables (see for example Woessmann 2004). Such variables are: student age, student sex, parent education dummies and dummies for community location of schools. As some of the “fundamental” variables had a very small number of missing values, these values were imputed by the median category.

8
Figure<sup>4: OED Ratings:Probabilityof Project Success</sup> <!-- Start of picture text --> OED Ratings: Probability of Project Success<br>0 - 50<br>53 - 60<br>63 - 76<br>77 - 89<br>_ 90- 100<br>- n.a.<br><!-- End of picture text --> Source:<sup>World Bank, OED</sup> 35
domestic saving to GDP in Mongolia for the period 1996-2005 (22 percent) is higher than the average for low income countries with comparable interest spreads between deposit and lending rates (Tables 6 and 7).

**Table 5:  Gross Domestic Savings** <u>(% of GDP)</u>

||**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**Period**<br>**Average**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Mongolia|22|34|22|23|21|19|11|18|25|26|22|
|Cambodia|-4|3|-1|4|5|11|12|16|15|..|7|
|Vietnam|17|20|22|25|27|29|29|27|28|..|25|
|Azerbaijan|3|10|2|13|21|24|27|30|30|..|18|
|Kazakhstan|15|13|11|20|26|26|27|31|35|37|24|
|Kyrgyz republic|-1|14|-6|3|14|18|14|5|4|0|7|
|Uzbekistan|23|19|20|17|19|20|22|27|32|33|23|
|Ghana|13|4|10|3|5|7|8|11|8|11|8|
|Uruguay|15|15|15|14|12|12|13|15|15|..|14|
|East Asia &<br>Pacific|37|37|36|34|34|34|35|37|38|..|36|
|Low income<br>countries|18|18|17|18|20|20|20|20|20|..|19|

_Source: World Bank (SIMA)._

**Table 6:  Interest Rate Spreads (lending minus deposit rate), 1996-2005**

||**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|
|---|---|---|---|---|---|---|---|---|---|---|
|Mongolia|43.2|45.7|19.3|15.9|15.9|15.9|15.2|12.3|11.2|10.6|
|Cambodia|10.0|10.4|10.5|10.2|10.5|12.1|13.7|16.5|15.8|15.4|
|Vietnam|20.1|5.9|5.2|5.3|6.9|4.1|2.6|2.9|..|3.9|
|Azerbaijan|..|..|..|7.4|6.8|11.2|8.7|5.9|6.5|8.5|
|Kyrgyz republic|28.3|9.8|37.7|25.3|33.5|24.8|18.9|14.1|22.6|20.8|
|Uruguay|43.5|37.6|31.6|28.5|27.8|27.0|55.8|29.3|17.5|10.8|
|East Asia & Pacific|6.1|6.2|5.6|5.9|6.9|5.9|5.9|5.9|6.3|5.5|
|Low income countries|11.5|10.6|12.7|12.7|12.9|13.7|13.0|12.4|11.9|11.0|
|Low & middle income|8.7|8.4|9.3|8.3|8.4|8.3|8.6|8.2|7.4|7.3|
|Middle income countries|7.6|7.4|7.7|7.5|7.6|7.6|7.2|7.1|6.5|6.5|
|High income countries|3.9|3.5|3.8|3.9|4.1|4.1|4.1|..|..|..|
|World|7.2|7.0|7.6|7.1|7.4|7.1|7.2|7.1|6.5|6.5|

_Source: World Bank (SIMA); Note: Data for Kazakhstan, Uzbekistan and Ghana were not available._ This increase in domestic saving has been due to the strong GDP growth and balance of payments position of Mongolia over the past few years. These, in turn, have been due to favorable terms of trade, new mining operations, strong tourism revenues, remittances from abroad, and robust capital flows in the mining sector.<sup>21</sup> Indeed, receipts from remittances have grown significantly since 1997 (when no remittance receipts were recorded) to a peak of US$195 million in 2004. But in 2005 remittances’ growth was negative for the first time since 1997, allegedly due to the closure of illegal representative

> 21 Remittances and proceeds from businesses contributed 30-40 percent of growth in deposits in 2005, according to data from BOM.

13
Bank (2005) data on sectoral value added,<sup>4</sup> and Purdue University’s Global Trade Analysis Project database (GTAP, 2005) on labor shares.

We focus on changes occurring over long horizons, where the poverty reductioneconomic growth relationship is most stable. For this reason we use only one spell per country, where the duration of the spell corresponds to the longest period for which initial and final poverty data exist for the country. The rest of the variables (e.g., value added growth rates and labor ratios) are calculated over the corresponding period per country.

The dependent variable is the proportional change in poverty over a period of time (spell) per country. Specifically, this is the annualized change in poverty as proportion to average poverty over the period.<sup>5</sup> Given its importance in the literature, the benchmark poverty measure in the paper is the headcount poverty index, defined as the fraction of the population with income below a given poverty line. In robustness exercises, however, we use alternative measures of poverty, comprising other members of the Foster-Greer-Thorbecke class of measures (the poverty gap and the squared poverty gap) and the Watts index. Following convention for cross-country comparability, the poverty line is set to $1 per person per day, converted into local currency using a purchasing-power-parity adjusted exchange rate.

Regarding the explanatory variables, we work with growth rates of sectoral valueadded and employment data at two levels of disaggregation. The first is the traditional sectoral division of agriculture, industry, and services. The second one disaggregates industry further into mining, manufacturing, utilities, and construction. Sectoral growth rates are calculated directly from data on sectoral value added as annualized log changes of per capita value added between the end and start of the corresponding spell. Employment data is calculated indirectly from data on sectoral value added and payments

> 4 The World Bank (2005) data on sectoral value added is complemented with statistics from the InterAmerican Development Bank and the United Nations.

> length of the spell; and the subscripts _I_ and _F_ , initial and final, respectively.  Calculating the proportional change with respect to the average measure allows us to avoid abnormally large proportional changes when very low initial and/or final measures are present, as would be the case if log differences were used.  Kraay (2005) uses the latter procedure and then is forced to drop a considerable number of observations.  Were we to use Kraay’s method, we would be working with 32 country observations, rather than 51, the sample size of our benchmark regression.

9
In columns (8) to (10) in table 2 we look at this in a regression setting with plant and time dummies. In column (8) the OLS coefficient of 0.127 implies increasing the adoption of management practices by 1 percentage point would be associated with about a 0.1% increase in output. In column (9), we see the impact of management instrumented with the intervention displays a higher significant point estimate of 0.346. As with quality and inventory the IV estimator is again notably higher than the OLS estimator, again indicating an endogenous adoption of better management when output falls. Finally, in column (10) we look at the ITT and see a point estimate of 0.056, implying a 5.4% increase in output (exp(0.056)-1), although this only significant at the 11% level.<sup>22</sup> Looking at the small-sample standard errors we find the IM and permutation tests are all significant at the 5% or 10% level.

There are several reasons for these increases in output. Undertaking routine maintenance of the looms reduces breakdowns. Collecting and monitoring the breakdown data also helps highlight looms, shifts, designs and yarn-types that are associated with more breakdowns. Visual displays around the factory floor together with the incentive schemes motivate workers to improve operating efficiency. Finally, keeping the factory floor clean and tidy reduces the number of untoward incidents like tools falling into machines or factory fires. Again the experience from lean manufacturing is that the collective impact of these procedures can lead to extremely large improvements in operating efficiency, raising output levels.

# **_V.D Results by plant_**

We can also examine the difference in quality, inventory and output after treatment on a plant by plant basis. Figure 6 plots the histograms of the before-after changes in our performance measures for the treatment and control plants. No outliers are driving these differences, with all treatment plants improving their quality (top-left plot), nine of the treatment plants improving their inventory (top-right plot) and all treatment plants improving their output (bottom left plot). In comparison the control plants appear to be fairly randomly distributed around the zero impact point. We can also test the statistical difference of these changes between the two groups, and

> 22 The IV is significant (and not the ITT) because the first stage of the IV uses log(cumulative treatment) rather than the binary 1/0 treatment variable, with the former more correlated with the gradual improvement in performance. Running the reduced-form for log(output) returns a coefficient (s.e.) of 0.028 (0.009) on log(cumulative treatment).

19
for example, be _GIC_ ( _τ_ ) for a different country, or period. Or it might be _GIC_<sup>_∗_</sup> ( _τ_ ) _._ The natural expedient of replacing the unknown _r_ in the test statistic by estimates introduces � some fundamental difficulties. The estimate will be denoted as _r_ ( _τ_ ). Let In this framework, we follow Chernozhukov and Fernandez-Val (2005) and assume that the quantile estimates and nuisance parameter estimates satisfy the following:<sup>_√_</sup> _<u>n</u>_ <u>-consistent</u> � estimators for _β_<sup>�</sup> ( _·_ ) and � _r_ ( _·_ ), such that<sup>_√_</sup> _<u>n</u>_ <u>(</u> _β_<sup>�</sup> ( _·_ ) _−β_ ( _·_ )) ⇝ _h_ (G( _·_ ))<sup>_′_</sup> and<sup>_√_</sup> _<u>n</u>_ <u>(</u> _r_ ( _·_ ) _−r_ ( _·_ )) ⇝ G _r_ ( _·_ ) jointly in _l_<sup>_∞_</sup> ( _T_ ), where ( _h_ (G( _·_ ))<sup>_′_</sup> _,_ G _r_ ( _·_ )) is a zero mean continuous Gaussian process with a � non-degenerate covariance kernel. Thus, we have that<sup>_√_</sup> _<u>n</u>_ <u>(</u> _β_<sup>�</sup> ( _τ_ ) _− r_ ( _τ_ )) ⇝ _h_ (G( _τ_ ))<sup>_′_</sup> _−_ G _r_ ( _τ_ ). The process remains asymptotically Gaussian; however, the estimation of _r_ ( _τ_ ) introduces a new drift component that additionally complicates the covariance kernel of the process.

Under the null hypothesis _H_ 0 : _β_ ( _τ_ ) = _r_ ( _τ_ ), the test statistics become:

**Example** (The uniformly constant (but unknown) effect hypothesis) **.** _A basic hypothesis is that the growth incidence curve, GIC_ ( _τ_ ) _, is statistically equal to the mean growth rate for all τ ∈T . , i.e., growth has no distributional heterogeneity. The alternative is that GIC_ ( _τ_ ) � � _differs from the mean at least for some τ ∈T . In this case, r_ ( _τ_ ) = _γAGR, (where γAGR is the mean growth rate)._

Now we display the limiting distributions of these test statistics under the null hypothesis.

**Lemma 3.** _Assume the conditions of Theorem 2 and that_<sup>_√_</sup> _<u>n</u>_ <u>(</u> _β_<sup>�</sup> ( _·_ ) _− β_ ( _·_ )) ⇝ _h_ (G( _·_ ))<sup>_′_</sup> _and_ � _√n_ <u>(</u> _r_ ( _·_ ) _− r_ ( _·_ )) ⇝ G _r_ ( _·_ ) _jointly in l_<sup>_∞_</sup> ( _T_ ) _, where_ ( _h_ (G( _·_ ))<sup>_′_</sup> _,_ G _r_ ( _·_ )) _is a zero mean continuous Gaussian process with a non-degenerate covariance kernel.. Under H_ 0 : _β_ ( _τ_ ) = _h_ ( _Q_ ( _τ_ )) = _r_ ( _τ_ ) _, τ ∈T , as n →∞,_

This result can be applied to test for the _GIC_ . The limiting distributions of the test statistics under the null hypothesis follow from Lemma 3 . Under the null hypothesis ( _H_ 0 : 18
correspond quite closely, but the commune-level numbers are more suspect. Errors in assigning the project funds appear to average out.

Much of the analysis in this paper focuses on a section of the commune questionnaire that asks about the kilometers of roads that were rehabilitated and built during the last two years. A potential data is that routine maintenance appears to have been recorded as rehabilitation for some communes.<sup>9</sup> We suspect that this is the case when the kilometers rehabilitated are unusually high and equal, or very nearly equal, the commune’s entire road or earth road network, the costs are low and the work has been entirely implemented by villagers’ labor contributions, with added funding from the commune only. The incidence of such cases is more common in the baseline which could indicate that road maintenance is declining over the period as more funds get allocated to rehabilitation. Or it could reflect a change in how the question was interpreted by the interviewers over time.

This potential data problem may affect selection into the project and project impacts. It can be conjectured that the demand for rehabilitation will tend to be low in places where maintenance is well done and that the project will be allocated to areas with a poor record in maintenance. However, in testing for this we found that such communes were no more (or less) likely to participate in the project. We set those pre-project rehabilitated kilometers to zero on the assumption that although roads were maintained, none were rehabilitated. This data issue may be more problematic for calculating impacts. So, we exclude communes with very large outliers during the project period. This leaves us with 98 project and 96 non-project communes.

In theory, for full fungibility to be possible, the amount allocated needs to be less than or equal to the amount being spent anyway. The recipient then spends what had been intended and the aid is treated as general budget support. We find that for the bulk of project communes ─ 82 out of 98 ─ the kilometers actually rehabilitated during the project period were greater than or equal to the kilometers allocated by RTP1.

Superficially then, these simple descriptive statistics establish that full fungibility was at

> 9  Routine maintenance refers to work that is not intended to cover major repairs or upgrades, and is required annually or periodically on all roads, rendering rehabilitation unnecessary. Rehabilitation is done on poor and very poor roads that have not received proper maintenance and might include regraveling, slope

9
Policy Research Working Paper 10083

# **Abstract**

Hardly any evidence exists on the effects of mental illness on refugee labor outcomes. This paper offers the first study on this topic in the context of Australia, one of the host countries with the largest number of refugees per capita in the world. Analyzing the _Building a New Life in Australia_ longitudinal survey, the paper exploits the variations in traumatic experiences of refugees interacted with post-resettlement time periods to causally identify the impacts of refugee mental health. The findings show that worse mental health, as measured by a one-standard-deviation increase in the Kessler mental health score, reduces the probability of employment by 14.1 percent and labor income by 26.8 percent. There is also evidence of adverse impacts of refugees’ mental illness on their children’s mental health and educational performance. These effects appear to be more pronounced for newly arriving refugees and those without social networks, but they may be ameliorated with government support.

This paper is a product of the Development Data Group, Development Economics and the Fragility, Conflict and Violence Global Theme. 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 hdang@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
Creating greater fiscal space to guarantee wage stability during crisis situations and adopting institutional and regulatory innovations to guide personnel hiring policies would greatly benefit the state and help reduce procyclicality. It is important to avoid large swings in the real wage of public servants (i.e. teachers, doctors, police officers) without objective reasons. A more accurate regulatory framework to govern hiring policies would also help prevent substantial increases in staff (typically occurring during economic upturns) that are challenging to reverse. However, in countries such as Argentina, where the wage bill is mainly decentralized across subnational administrations, coordinating these policies may be more challenging. Therefore, achieving more stable wage policies is contingent on establishing coordination mechanisms among different levels of government and ensuring the effective implementation of regulatory frameworks.

**Figure 8. Wage bill cyclicality per country 1992-2022 (or closest years)**

<!-- Start of picture text --> 1<br>EMERGING COUNTRIES ARG:<br>0,8 +0,65<br>0,6 ADVANED ECONOMIES<br>0,4<br>0,2<br>0<br>-0,2<br>-0,4<br>-0,6<br>-0,8<br>-1 Egypt Belgium Switzerland Luxembourg Australia Korea Moldova Indonesia New Zealand El Salvador UA Emirates Germany Denmark Canada Colombia Bolivia Chile Cabo Verde Norway Kazakhstan France Paraguay Costa Rica Albania Kosovo United Kingdom United States Thailand Peru Congo Austria Palau Armenia Netherlands Mongolia Slovak Republic Slovenia Finland Japan South Africa Italy Azerbaijan Romania Malta Bosnia Her Serbia Belarus Spain Turkey Singapore Bulgaria Poland Israel Mexico Brazil Russian Federation Ireland Croatia Czech Republic Estonia Lithuania Portugal Sweden Afghanistan Cyprus Argentina Ukraine Iceland Greece<br><!-- End of picture text --> _Source:_ Ministry of Economy, INDEC, GFS IMF, OECD. _Notes:_ the cyclical components of GDP and public spending have been estimated using the Hodrick-Prescott filter. Public spending is considered in real terms, deflated by CPI.

**Figure 9. Wage bill procyclicality: staff, average wage and level of government (1992-2022)**

<!-- Start of picture text --> 1,0<br><!-- End of picture text --> <!-- Start of picture text --> 0,9<br>0,8<br>0,7<br>0,6<br>0,5<br>0,4<br>0,3<br>0,2<br>0,1<br>0,0<br>National Administration Provinces<br><!-- End of picture text --> _Source:_ Ministry of Economy, INDEC. _Notes:_ the cyclical components of GDP and public spending have been estimated using the HodrickPrescott filter. Public spending is considered in real terms, deflated by CPI. *, ** and *** indicate statistical significance at the 15%, 10% and 5% levels, respectively.
|P082957|2004|2008|12/19/2007|7 Closed|Mali|
|---|---|---|---|---|---|
|P082957|2004|2008|6/18/2008|8 Closed|Mali|
|P082957|2004|2009|12/30/2008|9 Closed|Mali|
|P082957|2004|2009|6/30/2009|10 Closed|Mali|
|P082957|2004|2010|12/22/2009|11 Closed|Mali|
|P082957|2004|2010|4/27/2010|12 Closed|Mali|
|P082957|2004|2011|3/25/2011|13 Closed|Mali|
|P082957|2004|2012|12/28/2011|14 Closed|Mali|
|P082957|2004|2020|4/29/2020|15 Closed|Mali|
|P082969|2007|2007|1/3/2007|1 Closed|Gambia,The|
|P082969|2007|2007|6/28/2007|2 Closed|Gambia,The|
|P082969|2007|2008|11/30/2007|3 Closed|Gambia,The|
|P082969|2007|2008|5/28/2008|4 Closed|Gambia,The|
|P082969|2007|2009|12/12/2008|5 Closed|Gambia,The|
|P082969|2007|2009|3/19/2009|6 Closed|Gambia,The|
|P082969|2007|2010|7/30/2009|7 Closed|Gambia,The|
|P082969|2007|2010|2/8/2010|8 Closed|Gambia,The|
|P082969|2007|2010|4/30/2010|9 Closed|Gambia,The|
|P082969|2007|2011|11/6/2010|10 Closed|Gambia,The|
|P082969|2007|2011|3/29/2011|11 Closed|Gambia,The|
|P082969|2007|2013|12/29/2012|12 Closed|Gambia,The|
|P082969|2007|2020|4/23/2020|13 Closed|Gambia,The|
|P082973|2005|2005|5/2/2005|1 Closed|Colombia|
|P082973|2005|2006|4/26/2006|2 Closed|Colombia|
|P082973|2005|2007|11/20/2006|3 Closed|Colombia|
|P082973|2005|2007|6/4/2007|4 Closed|Colombia|
|P082973|2005|2008|12/20/2007|5 Closed|Colombia|
|P082973|2005|2008|6/20/2008|6 Closed|Colombia|
|P082973|2005|2009|12/9/2008|7 Closed|Colombia|
|P082973|2005|2009|4/28/2009|8 Closed|Colombia|
|P082973|2005|2010|5/24/2010|9 Closed|Colombia|
|P082973|2005|2011|2/12/2011|10 Closed|Colombia|
|P082973|2005|2012|8/2/2011|11 Closed|Colombia|
|P082973|2005|2012|12/28/2011|12 Closed|Colombia|
|P082976|2004|2004|3/31/2004|1 Closed|Yemen,Republic of|
|P082976|2004|2005|4/11/2005|3 Closed|Yemen,Republic of|
|P082976|2004|2006|9/28/2005|4 Closed|Yemen,Republic of|
|P082976|2004|2006|3/9/2006|5 Closed|Yemen,Republic of|
|P082976|2004|2006|6/13/2006|6 Closed|Yemen,Republic of|
|P082976|2004|2007|12/28/2006|7 Closed|Yemen,Republic of|
|P082976|2004|2007|6/18/2007|8 Closed|Yemen,Republic of|
|P082976|2004|2008|12/7/2007|9 Closed|Yemen,Republic of|
|P082976|2004|2008|6/3/2008|10 Closed|Yemen,Republic of|
|P082976|2004|2009|12/30/2008|11 Closed|Yemen,Republic of|
|P082976|2004|2009|6/29/2009|12 Closed|Yemen,Republic of|
|P082976|2004|2010|12/28/2009|13 Closed|Yemen,Republic of|
|P082976|2004|2010|4/24/2010|14 Closed|Yemen,Republic of|