rafmacalaba/datause-encoder-data huggingface dataset:
parents in many contexts<sup>are conservative about teachingmethods,and theformula for</sup> compensation does not pay<sup>schools forinnovationor professional development.</sup> Constraints external to the<sup>school,such asthe selection criteria of higher-levelschoolsor</sup> universities, might also limit<sup>theinterest schoolswill take in developingnew methods.</sup> Curricular innovation might<sup>wellbe moreeasily observed than overalleffort,so its</sup> inclusion in the compensation<sup>schememight makesense.</sup>

## _The Value of Monitoring_

With intense incentives,<sup>it pays to monitor theagent's performancecarefully</sup> (Milgrom and Roberts 1992).<sup>In a voucherprogram, a schoolwill be rewardedif it can</sup> exaggerate enrollment rates<sup>and get away with it. It willalso be rewardedif it</sup> successfully lowers standards<sup>for expellingstudents,or if it raises the averagegrades</sup> students can expect to receive,<sup>which in turn entices more of them to choose the school.</sup> Without reliable inspectors<sup>and a strong currentof professionalismamong educators,</sup> education ministries in developing<sup>countries will be hard pressedto monitor how schools</sup> behave in response to intense<sup>incentives,which in turn will lower the usefulnessof those</sup> incentives. If the costs of<sup>settingup a functioningmonitoring systemare sufficiently</sup> high, they will outweigh<sup>the benefitof imposing intense incentives, unless, of course, the</sup> establishment of a monitoring<sup>system itself enhancesprofessionalismand carries its own</sup> educational benefits.

# **3. Voucher Programs in Industrialized**<sup>**Countries**</sup>

While the expected effects<sup>of voucherprograms listedabovewere taken directly</sup> from theory, empirical work<sup>on voucher programsin industrializedcountries supports the</sup> usefulness of the framework.<sup>It should be emphasized,as the last sectionsuggested,that</sup> voucher programs take a variety<sup>of forms,dependingon,among other things, rules</sup> regarding student eligibility,<sup>student admissions, informationdissemination, fiscaland</sup> pedagogical accountability,<sup>the meanand the distributionof thevoucher value,and</sup> school-level management<sup>autonomy, which willall have an impacton the expected</sup> impact of any given program<sup>on academic achievement, equity, innovation,and the</sup> state's other educational<sup>objectives. Impact evaluationsof voucher programsremain</sup> sparse. Still, experiences<sup>with voucherprograms can be analyzed to determine whether or</sup> not they provide support<sup>for the principal-agentapproach.</sup> If enrollments are a biased<sup>or noisyindicator of schooleffort,then the expected</sup> impact of voucher programs<sup>on school effort is likelyto beweak,with little final impact</sup> on outcomes. In the United<sup>Statesstudies of the academiceffect of voucher programsin</sup> small, privately funded efforts<sup>in New YorkCity,Washington DC, Dayton,and</sup> 9
|**Table**|**8.3 SOUTH AF**<br>**variables**<br>|**RICA, Descriptive**<br>**under different i**<br>|**statisti**<br>**mputati**<br>|**cs of produ**<br>**on mechani**<br>|**ction fu**<br>**sm**<br>|**nction**<br>|
|---|---|---|---|---|---|---|
||**Variable**|**#Obs. (#imputed)**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|Sales|Complete case|1578|17.43|1.86|8.28|24.29|
||ICA method|1587 (9)|17.44|1.87|8.28|24.29|
||Random ICA meth.|1587 (9)|17.44|1.87|8.28|24.29|
||EM alg. [1]|1789 (211)|17.42|1.81|8.28|24.29|
||EM alg. [2]|1587 (9)|17.44|1.87|8.28|24.29|
||EM alg. [3]|1587 (9)|17.44|1.87|8.28|24.29|
|Materials|Complete case|1508|16.59|2.03|3.56|24.21|
||ICA method|1587 (79)|16.60|2.00|3.56|24.21|
||Random ICA meth.|1587 (79)|16.66|2.01|3.56|24.21|
||EM alg. [1]|1789 (281)|16.58|1.93|3.56|24.21|
||EM alg. [2]|1586 (78)|16.59|2.08|3.56|24.21|
||EM alg. [3]|1586 (78)|16.59|2.08|3.56|24.21|
|Capital|Complete case|1337|15.29|1.89|7.90|23.48|
||ICA method|1586 (249)|15.25|1.86|7.90|23.48|
||Random ICA meth.|1586 (249)|15.60|1.90|7.90|23.48|
||EM alg. [1]|1786 (449)|15.24|1.75|7.90|23.48|
||EM alg. [2]|1583 (246)|15.20|1.84|7.90|23.48|
||EM alg. [3]|1580 (243)|15.22|1.87|7.90|23.48|
|Employment|Complete case|1664|12.12|1.40|5.19|17.47|
||ICA method|1685 (21)|12.12|1.40|5.19|17.47|
||Random ICA meth.|1685 (21)|12.13|1.40|5.19|17.47|
||EM alg. [1]|1784 (120)|12.10|1.40|5.19|17.47|
||EM alg. [2]|1680 (16)|12.13|1.40|5.19|17.47|
||EM alg. [3]|1680 (16)|12.13|1.40|5.19|17.47|

The null hypothesis of the one-sample Kolmogorov-Smirnov Test is that the cumulative distribution differs from the hypothesized theoretical normal distribution. Source: Authors’ estimations with ICSs data.

61
# Savings and the Terms of Trade under Borrowing Constraints

Pierre-Richard Agenor* and Joshua Aizenman**

## JEL Classification Numbers: D91, F41,<sup>055</sup>

*The World Bank, Washington DC 20433;<sup>'*Department of Economics, Dartmouth</sup> College, Hanover NH 03755, and NBER. Some<sup>of the ideas developed in this paper origi-</sup> nate from background work on the 1999 _Global_<sup>_Economic Prospects_Report of the World</sup> Bank. We would like to thank, without implication,<sup>Mustapha Nabli, William Shaw, and</sup> Robert Keyfitz for useful discussions and Nihal Bayaktar<sup>for excellent research assistance.</sup>
Policy Research Working Paper 10783

# **Abstract**

What drives citizen’s trust in state institutions? There are longstanding debates on the pathways towards institutional trust: is trust driven by citizen’s perceptions of policy outcomes or by their perceptions of the integrity and credibility of policy processes? This paper investigates this question using data from a large-scale survey of 5,916 Moroccans and argues that process matters more than outcomes for trust-building. The paper first shows that Moroccans’ trust in institutions is strongly associated with positive evaluations of policy outcomes—including satisfaction with the delivery of public goods and services and with government’s economic performance. It then provides evidence that institutional trust is even more strongly and robustly associated with the quality of governance processes, and in particular with the perception that institutions function with integrity and make credible commitments. Going beyond policy variables, the paper also provides complementary evidence that institutional trust is contingent on individual-level social capital, including social trust, and socio-demographic factors. The conclusion briefly lays out the policy implications of this research and areas for future investigation.

This paper is a product of the Governance Global Practice. 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 diane.zovighian@sciencespo.fr, mcloutier1@worldbank.org, and above@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
Table A2. Variance decompositions of global factors

||Globa<br>16%|l Inflation<br>Median|factor<br>84%|Global<br>16%|output grow<br>Median|th factor<br>84%|Global<br>16%|interest rat<br>Median|e factor<br>84%|
|---|---|---|---|---|---|---|---|---|---|
|Canada|26.0|30.1|34.4|33.5|36.4|39.1|53.8|63.3|73.9|
|France|46.0|52.8|58.1|51.3|54.6|58.0|11.0|14.9|20.7|
|Germany|14.7|17.5|20.2|42.6|45.5|48.5|11.2|15.2|21.2|
|Italy|18.4|21.6|24.7|20.7|23.2|25.5|2.5|4.2|7.1|
|Japan|8.0|10.1|12.5|8.8|10.5|12.5|1.6|2.9|5.0|
|United Kingdom|20.7|24.5|28.5|46.2|49.2|51.9|7.7|10.6|14.4|
|United States|35.4|40.9|46.5|46.7|49.7|53.4|41.4|48.4|54.7|
|Average|24.2|28.2|32.1|35.7|38.4|41.3|18.5|22.8|28.1|

Note: Global output growth factor, global interest rate factor, and global inflation factor are extracted from detrended national industrial production growth rates, short-term interest rates (1<sup>st</sup> differenced), and inflation rates in G7 countries, respectively, using a dynamic factor model.

52
lending further evidence in favor of the reliability of farmers’ self-reports in the face of a severe drought.

_Table 15_ : Ethiopia: Regression of self-reported drought exposure on measured rainfall intensity and controls.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|ESS13<br>Maize|ESS13<br>Sorghum|ESS15<br>Maize|ESS15<br>Sorghum|ESS18<br>Maize|ESS18<br>Sorghum|
|12-month total rainfall (mm)|-0.000247***|-0.000608***|-0.000672***|-0.000744***|-0.000103|-0.000147|
||(6.22e-05)|(0.000151)|(6.78e-05)|(8.03e-05)|(9.95e-05)|(0.000123)|
|Soil fertility index|-0.00704|-0.00305|-0.0350|-0.0175|-0.0131|0.0508|
||(0.0136)|(0.0199)|(0.0267)|(0.0299)|(0.0291)|(0.0550)|
|Plot Potential Wetness Index|-0.00352|0.00108|-0.00820|-0.00776|0.00138|0.00158|
||(0.00328)|(0.00616)|(0.00739)|(0.00754)|(0.00432)|(0.00581)|
|Plot Slope (percent)|0.000656|8.42e-05|-0.000754|-0.000852|-0.00567***|-0.00652**|
||(0.00101)|(0.000938)|(0.00211)|(0.00197)|(0.00217)|(0.00259)|
|Intercropped|-0.0226|-0.0780**|-0.0349|0.0435|0.0563|0.00169|
||(0.0166)|(0.0381)|(0.0343)|(0.0389)|(0.0351)|(0.0340)|
|Erosion control|-0.00801|-0.0558|-0.0118|-0.00165|0.0692*|-0.0303|
||(0.0194)|(0.0452)|(0.0419)|(0.0438)|(0.0395)|(0.0439)|
|Any organic fertilizer|0.00545|0.0600|0.0395|0.0374|-0.0207|0.0266|
||(0.0175)|(0.0514)|(0.0272)|(0.0382)|(0.0292)|(0.0441)|
|Any inorganic fertilizer on plot|-0.0739**|0.0319|-0.0716|0.0977*|-0.00839|0.0271|
||(0.0296)|(0.0416)|(0.0504)|(0.0580)|(0.0295)|(0.0583)|
|Improved seed|0.0397|0.179|0.0814|0.0511|0.00775|-0.0621|
||(0.0356)|(0.161)|(0.0499)|(0.0486)|(0.0437)|(0.0773)|
|Constant|0.706***|1.245***|1.516***|1.501***|0.192|0.264|
||(0.149)|(0.276)|(0.160)|(0.166)|(0.137)|(0.183)|
|Observations|2,908|2,342|2,931|2,344|1,605|1,210|
|Adjusted R-squared|0.293|0.220|0.383|0.498|0.115|0.384|
|Region FE|YES|YES|YES|YES|YES|YES|

Linear Probability Model regressing self-reported drought exposure on 12-month rainfall intensity and a number of controls that are potentially relevant for drought exposure. Clustered standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table 15 and Table A 12 show regressions of self-reported crop damage due to drought on 12month total rainfall intensity and a number of controls hypothesized to be relevant for plot-level drought exposure (Nkonya _et al._ , 2018; Iizumi and Wagai, 2019; Lombardi _et al._ , 2019; McCarthy _et al._ , 2021). The findings suggest that self-reports are significantly associated with low measured rainfall in the year in which the crop was planted and grown. Particularly in the 2015/16 Ethiopian Meher season, which featured widely reported drought (Philip _et al._ , 2018; Sohnesen, 2020), observed rainfall explains a considerable share of the variation in farmers’ self-reports based on the adjusted R<sup>2</sup> . However, there is no robust set of plot-level predictors of self-reported drought exposure. This, in turn, leaves a significant share of the variation unexplained by observable 41
# **Table 12.  Sensitivity check for the by-size results: non-state domestic and entrepreneurial CEOs only**

|||**Sma**|**ll**|||**Medi**|**um**|||**Larg**|**e**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**without**<br>**growth opp**|**base**|**IV**|**More**<br>**controls**|**without**<br>**growth opp**|**base**|**IV**|**More**<br>**controls**|**without**<br>**growth opp**|**base**|**IV**|**More**<br>**controls**|
|ln(GDP per capita)|-0.105**|-0.105**|-0.131**|-0.127**|-0.006|-0.007|-0.017|-0.005|0.015|0.017|0.022|-0.004|
||(-1.963)|(-1.996)|(-2.383)|(-1.997)|(-0.181)|(-0.220)|(-0.600)|(-0.161)|(0.483)|(0.540)|(0.736)|(-0.139)|
|ln(city pop)|-0.012|-0.001|-0.011|-0.038|0.022|0.015|0.027|0.040|0.039|0.037|0.035|0.003|
||(-0.237)|(-0.031)|(-0.205)|(-0.773)|(0.873)|(0.580)|(1.157)|(1.188)|(1.359)|(1.209)|(1.145)|(0.179)|
|ln(firm age)|-0.094**|-0.096**|-0.092**|-0.083**|-0.032**|-0.037**|-0.031**|-0.032**|-0.027*|-0.019|-0.024*|-0.022*|
||(-2.389)|(-2.366)|(-2.569)|(-2.065)|(-2.015)|(-2.249)|(-2.174)|(-1.980)|(-1.929)|(-1.367)|(-1.688)|(-1.799)|
|Loan|0.286***|0.306***|0.275***|0.273***|0.155***|0.161***|0.150***|0.141***|0.039|0.059|0.058|0.063*|
||(4.603)|(4.979)|(4.363)|(4.506)|(6.437)|(6.653)|(6.008)|(5.682)|(0.948)|(1.534)|(1.397)|(1.940)|
|Trade credit|0.275*|0.265|0.201|0.272*|0.026|0.026|0.031|0.011|0.042|0.044|0.060|0.055|
||(1.693)|(1.606)|(1.334)|(1.652)|(0.353)|(0.340)|(0.453)|(0.153)|(0.640)|(0.688)|(0.893)|(0.910)|
|UCA|0.047***|0.048***|0.036***|0.050***|0.051***|0.050***|0.051***|0.054***|0.041***|0.039***|0.053***|0.038***|
||(3.778)|(3.794)|(2.861)|(3.948)|(5.682)|(5.572)|(5.410)|(5.794)|(3.325)|(3.171)|(3.393)|(3.349)|
|sales/lagged K|0.008***|0.009***|0.011***|0.008***|0.011***|0.010***|0.007|0.011***|0.003|0.003|-0.007|0.003|
||(4.070)|(4.225)|(4.351)|(3.768)|(2.883)|(2.635)|(1.358)|(2.790)|(0.639)|(0.721)|(-1.139)|(0.684)|
|CF/lagged K|0.102***|0.104***|0.046|0.109***|0.163***|0.164***|0.219***|0.166***|0.275***|0.280***|0.483***|0.253***|
||(3.235)|(3.269)|(1.146)|(3.359)|(4.051)|(3.953)|(4.250)|(3.998)|(5.669)|(5.643)|(4.684)|(5.614)|
|Lagged sale<br>growth||0.035*||||0.003||||0.031***|||
|||(1.960)||||(0.371)||||(2.845)|||
|Avg ind Q||0.360*||||0.239**||||0.064|||
|||(1.813)||||(2.142)||||(0.779)|||
|area dummies|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|yes|
|other controls|no|no|no|yes|no|no|no|yes|no|no|no|yes|
|Number of<br>observations|2,096|2,046|2,036|2,033|3,766|3,683|3,706|3,676|1,389|1,364|1,377|1,359|
|Adjusted R2|0.070|0.080|0.062|0.077|0.080|0.082|0.074|0.087|0.064|0.076|0.051|0.075|

*** p<0.01, ** p<0.05, * p<0.10.    Not reported are coefficients of area dummies.  The instrument in the IV columns is once-lagged (CF/lagged K).  Other controls in “more control” columns include: loss of sales due to electricity, loss of sales due to transportation, road density, autonomy in investment, CEO schooling, CEO also board chairman, CEO wage/worker wage, CEO has incentive plans, the property rights protection index,

53
# **7. Discussion and policy implications**

The previous analysis has shown that while child mortality for the population as a whole has continued to fall under Doi Moi, progress has been much faster at the top end of the income distribution. Indeed, the poorest 25 percent of children saw virtually no improvements in their survival prospects at all in the mid-late 1 990s. The recent slow rate of decline of child mortality among poorer Vietnamese children, which is apparently due to a variety of factors, makes it doubtful whether Vietnam will achieve the reductions in infant and under-five mortality envisaged by the international development goals (IDGs). In this last section, we discuss some policy options for accelerating the pace of decline of child mortality among Vietnam's poor.

The previous sections showed that in two key respects the poorest 60 percent of Vietnamese children appear to slip backwards between 1993 and 1998-their mothers were less educated than their predecessors; and they were less likely to be delivered by trained birth attendants and in medical facilities. What would the impact of reversing these declines be on child survival prospects among the poorest 25 percent of Vietnamese children? In 1993, mothers in the bottom quartile averaged 5.8 years of schooling. In 1998, this figure had fallen to 5.4. In 1993, 62.7 percent of births in the poorest quartile were attended by a medically trained person and 43.1 percent of births took place in a medical facility. In 1998 these figures had fallen to 57.3 and 33.3 percent, respectively. Applying the 1998 coefficient vector to the 1993 values of these variables, keeping the other variables at their 1998 values, gives us an estimalte of how far mortality would fall if these declines were reversed. The figures in Table 1 1 indicate that the impact on the U5MR of reversing the decline in maternal schooling wvould be smaller than the impact of reversing the decline in deliveries attended by medical personnel and deliveries in medical facilities. Reversing both sets of changes togelher would reduce the U5MR by around 5.4%wo-an eleven per cent reduction in the U5MR.

**Table 11. Reversing declines in determinants of survival among poorest** 25 **percent**

||IMR|U5MR|
|---|---|---|
|Valuesamongpoorest25percentin1998|34.6|50.2|
|Mother'seducationbackto1993value|34.1|49.4|
|Attendeddeliveriesanddeliveriesatmedical facilitybackto1993values|31.3|45.4|
|Bothmother'seducationand deliveriesbackto1993values|30.8|44.8|

We consider next the effects of targeted improvements in health services, drinking water and sanitation. In all three areas the poorest 25 percent of children lag far behind the richest 75 percent, and closing these gaps-by bringing the poor up the levels enjoyed 29
remained between 78 and 90 percent. No country for which data were available exhibited a decline in collection rates.

**Natural Gas: Hidden Costs - Total**

<!-- Start of picture text --> Percent GDP<br>6.0<br>5.5<br>5.0<br>4.5<br>4.0<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2000 2001 2002 Average 2000 Average 2001 Average 2002<br>Armenia Azerbaijan Belarus Bulgaria Croatia GeorgiaKazakhstanKyrgyz Republic Moldova Poland Romania Russia Tajikistan Turkey UkraineUzbekistan<br><!-- End of picture text -->

# **_Water Sector_**

During 2002, hidden costs (in absolute terms) comprised—unaccounted losses, 50 percent; tariffs set below cost, 43 percent; poor collections, 7.0 percent. Hidden costs in all countries and for all years are below 2.5 percent of GDP, except Armenia and Moldova. In relative terms, hidden costs in the region overall, as a proportion of GDP, increased from 0.9 percent in 2000 to 1.2 percent in 2002. However, the aggregate value of hidden costs in this sector declined between 2000-02 by about 30 percent, from US$ 2.5 billion in 2000 to US$ 1.8 billion in 2002. The largest declines in hidden costs were for Russia and Tajikistan—a decline of around 70 percent in each country in the same period.

- _Water—unaccounted losses._ Former Soviet Union countries contributed most to an increasing trend of hidden costs due to unaccounted losses, which rose from an average of 0.6 percent of GDP in 2000 to 0.9 percent in 2002; from US$ 626 million to US$ 939 million. This is consistent with deteriorating integrity of supply networks evidenced by a rise in total system losses (water abstracted minus water billed) from 32 percent in 2000 to 39 percent in 2002. Losses increased the most in FSU-Low Income countries, perhaps indicating deteriorating supply networks.<sup>4</sup>

- _Water—tariffs and cost recovery._ Overall hidden costs due to tariffs set below cost-recovery levels decreased 31 percent from US$ 1.140 million to US$ 785 million, led by FSU-Middle Income countries (55 percent over the period). However, data for 2003, while not covering all countries, showed an overall increasing trend. As a percentage of GDP this has remained on average between 0.4-0.5 percent.

> 4 Although in absolute terms losses in Azerbaijan were the lowest across all countries.

9
Harmon, Colm, Hessel Oosterbeek, and Ian Walker. 2003. "The Returns to Education: Microeconomics." _Journal of Economic Surveys_ 17(2): 115-155.

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"Infectious Disease Threat Remains" 2006a. _Oxford Analytica._ June 30a.

"Infectious Disease: Preparing for the Future" 2006b. _Science._ September 8b. Jamison, Dean T., Lawrence J. Lau, and Jia Wang. 2005. "Health's Contribution to Economic Growth in an Environment of Partially Endogenous Technical Change," in Lopez-Casasnovas, Guillem, Berta Rivera, and Luis Currais (eds.), _Health and Economic Growth_ . Cambridge, MA: MIT University Press.

Janssen, Fanny, Anton E. Kunst, and Johan P. Mackenbach. July 2006. "Association Between Gross Domestic Product Throughout the Life Course and Old-Age Mortality Across Birth Cohorts: Parallel Analyses of Seven European Countries, 1950-1999." _Social Science & Medicine_ 63(1): 239-254.

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48
WPS4031

# Duration of Unemployment Benefits and Quality of Post-Unemployment Jobs:

# Evidence from a Natural Experiment

## Jan C. van Ours *             Milan Vodopivec † ‡

### World Bank Policy Research Working Paper 4031, October 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*Tilburg University, CentER, IZA, and CEPR; corresponding author: Department of Economics, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands; email: vanours@uvt.nl

†World Bank; email: Mvodopivec@worldbank.org ‡The authors wish to thank the National Employment Office of Slovenia, the Statistical Office of Slovenia and the Pension and Disability Fund of Slovenia for providing the data used in the study. Jakob Tomse provided excellent assistance in setting up the data sets. Support form the World Bank research project “Incentive Effects of UI systems in Transition Countries” (RFP087059- RESE-BBRSB) is gratefully acknowledged.