rafmacalaba/datause-encoder-data huggingface dataset:
guarantees for local government bailouts were perceived to be contributing factors for widespread subnational defaults in Russia in the late 1990s and in Mexico in the mid1990s.

# **3. Fiscal Adjustment: Case of Tamil Nadu**<sup>11</sup>

# **_3.1 Origin of fiscal crisis_**

In 1998/99 Tamil Nadu experienced an unprecedented fiscal crisis. Revenue, primary, fiscal and consolidated deficits grew rapidly (Figures 1 and 3), as well as debt and contingent liabilities (Figure 2).<sup>12</sup> In 2001/02 the debt stock as share of GSDP was 30 percent of GSDP after including state guarantees to back the market borrowing of lossmaking, state-owned enterprises. Debt accumulation led to an increase in interest expenditure to about 20 percent of revenue receipts – a threshold used by the central government to identify “debt stressed” states (Figure 4).

**Figure 4. Interest Expenditure as Percentage of Revenue Receipts**

<!-- Start of picture text --> 25.0%<br>20.1% 20.1%<br>20.0% 19.0%<br>16.9% 17.1%<br>15.0%<br>15.0% 12.7% 12.7% 13.2% 13.5% 13.1%<br>10.5%<br>9.6% 9.8%<br>10.0%<br>5.0%<br>0.0%<br>Percent<br>1990/91 1991/92 1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/00 2000/01 2001/02 2002/03 2003/04<br><!-- End of picture text --> Source: Government of Tamil Nadu’s Budget documents.

The growing deficit and debt were worrisome because recurrent expenditure rapidly outgrew revenues. As a result, a high and growing portion of the government net borrowing was used to finance consumption. While a growing fiscal deficit itself may not be a source of concern if the deficit is to finance capital expenditure, a large and growing

> 11 For a more detailed account of fiscal crisis and reforms, see World Bank (2005a).

> 12 Revenue deficit is the amount of current expenditure (such as wages, pension outlays, subsidies, transfers, and operation and maintenance) net of total revenues. Consolidated fiscal deficit captures off-budget activities and hidden liabilities, including off-budget borrowing serviced by the state government, capital works executed in the Public Account but financed by off-budget borrowings deposited in the Public Account, and the financial accounts of the state power utility.

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community questionnaires that we use to identify geographic effects. Most surveys spanned a full year. All are nationally representative with the exception of the India REDS which is representative of rural areas only. As can be seen in column 3, some countries, particularly in Sub-Saharan Africa and South Asia, are markedly more rural than others.

_Household access to water_ : As noted, water access in these countries is known to be limited. In Sub-Saharan Africa, Uganda, Madagascar, and Rwanda have among the highest shares of rural households that rely on a source of drinking water at least 30 minutes away (see WHO, 2008). Yemen and Morocco have among the lowest rates of per capita freshwater availability across the Middle East and North Africa (World Bank, 2005b). In South Asia, recent investments have vastly improved drinking water availability, yet large shares of the rural population continue to endure frequent water shortages and spend hours a day in water collection (Loughran and Pritchett, 1997; National Commission for Women, India, 2005).

In common with other studies (Whittington et al., 1990; Ilahi and Grimard, 2000; Kremer et. al., 2009), we define access to water by the household's reported time to walk one-way (in minutes) to the source of drinking water it typically uses. In the sole survey where time to water is not available (Rwanda), we use the distance in meters (converted to kilometers in the analysis). Time is preferred since it reflects potential difficulties in terrain. All households across the surveys were asked this question in a module on housing characteristics and infrastructure. Households with water inside the home or plot are considered to face zero walking time/distance.

This measure of access is particularly useful since it is continuous and directly related to the potential time constraints of rural individuals. In contrast to self-reported time spent on water collection from time use modules, this measure allows better comparability across households since it is not a function of the health and availability of household members, or reflective of other activities that may be combined with water collection.<sup>9</sup> As with other reported household variables, the time/distance variables are likely to suffer from measurement error. One mitigating factor is that households in the

> 9 Alternative measures include the type of water source, used by the WHO (although Ilahi (2001) shows a weak association between type and time use), and household views about the quality of their access to water.

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# **V.   REFERENCES**

<mark>Ausubel, L. M. (1991). The failure of competition in the credit card market.</mark> _<mark>The American Economic Review</mark>_ <mark>, 50-81.</mark> <mark>Adrian, T. and Shin, H.S., 2010. Financial intermediaries and monetary economics. In</mark> _<mark>Handbook of monetary economics</mark>_ <mark>(Vol. 3, pp. 601-650). Elsevier.</mark> <mark>Aikman, D., Haldane, A. G., & Nelson, B. D., 2014. Curbing the credit cycle.</mark> _<mark>The Economic Journal</mark>_ <mark>.</mark> <mark>Basistha, A. and Startz, R., 2008. Measuring the NAIRU with reduced uncertainty: a multipleindicator common-cycle approach.</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>,</mark> _<mark>90</mark>_ <mark>(4), pp.805-811.</mark> <mark>Claessens, S., Kose, M.A. and Terrones, M.E., 2012. How do business and financial cycles interact?.</mark> _<mark>Journal of International economics</mark>_ <mark>,</mark> _<mark>87</mark>_ <mark>(1), pp.178-190.</mark> <mark>Claessens, S., Kose, M.A. and Terrones, M.E., 2009. What happens during recessions, crunches and busts?.</mark> _<mark>Economic Policy</mark>_ <mark>,</mark> _<mark>24</mark>_ <mark>(60), pp.653-700.</mark> <mark>Drehmann, M., Borio, C.E. and Tsatsaronis, K., 2012. Characterising the financial cycle: don't lose sight of the medium term!.</mark> <mark>Edge, R.M. and Meisenzahl, R.R., 2011. The unreliability of credit-to-GDP ratio gaps in realtime: Implications for countercyclical capital buffers.</mark> _<mark>International Journal of Central Banking</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(4), pp.261-298.</mark> <mark>Gertler, M. and Karadi, P., 2011. A model of unconventional monetary policy.</mark> _<mark>Journal of monetary Economics</mark>_ <mark>,</mark> _<mark>58</mark>_ <mark>(1), pp.17-34.</mark> <mark>Green, E. J., & Porter, R. H. (1984). Noncooperative collusion under imperfect price information.</mark> _<mark>Econometrica: Journal of the Econometric Society</mark>_ <mark>, 87-100.</mark> <mark>Gorton, G. B., & He, P. (2008). Bank credit cycles.</mark> _<mark>The Review of Economic Studies</mark>_ <mark>,</mark> _<mark>75</mark>_ <mark>(4), 1181-1214.</mark> <mark>Hanson, S.G., Kashyap, A.K. and Stein, J.C., 2011. A macroprudential approach to financial regulation.</mark> _<mark>Journal of economic Perspectives</mark>_ <mark>,</mark> _<mark>25</mark>_ <mark>(1), pp.3-28.</mark> <mark>Hartmann, D.L., 2015.</mark> _<mark>Global physical climatology</mark>_ <mark>(Vol. 103). Newnes.</mark> <mark>Jordà, Ò., Schularick, M. and Taylor, A.M., 2013. When credit bites back.</mark> _<mark>Journal of Money, Credit and Banking</mark>_ <mark>,</mark> _<mark>45</mark>_ <mark>(s2), pp.3-28.</mark> <mark>Jordà, Ò., Schularick, M.H. and Taylor, A.M., 2011.</mark> _<mark>When credit bites back: leverage, business cycles, and crises</mark>_ <mark>(No. w17621). National Bureau of Economic Research.</mark>
Figure 40: Proportion of Total Crop Produce Sold by Yield Quantile for Millet and Sorghum, 2014/15 <!-- Start of picture text --> Millet Sorghum<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5<br>Yield Quantile<br>Proportion sold<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15.

# Farm Size

As has been observed in previous studies from around the world, yield and plot area have a strong negative relationship (Figure 41). This phenomenon, known as the inverse farm-size productivity relationship, can be attributed to a variety of factors, though no definite explanation exists. Reasons cited for this relationship include market failures (Feder 1985; Sen 1966), omitted variables related to the quality of the land (Benjamin 1995; Bhalla and Roy 1988), and measurement error relating to the size of plots (Lamb 2003). Key omitted variables include farmer effort and farm management. Larger farms employ outside labor who may shirk work or not be as motivated as household-supplied labor, which is the norm in household farms with smaller plot sizes.

This negative relationship was observed for Sudan and its separate regions, for all four major crops (Figure 41). These findings might suggest the need for land reform, to create smaller average landholding size to spur productivity growth, though the benefits of such a policy need to be carefully weighed against its possibly harmful repercussions. However, these results, and those in the following regression analysis, imply that larger plots are not necessarily helpful for increasing yields.

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# **B.- CORPORATE GOVERNANCE DIMENSIONS**

# **COMPONENT 1: OWNERSHIP AND LEGAL FRAMEWORK**

We privilege a legal framework in which companies are corporatized and subject to similar standards as other private companies. We also give priority to companies whose policies are established and monitored by a specialized government agency. The index gives higher scores to companies regulated by independent commissions or agencies and subject to the same tax obligations as any other private enterprise. The public listing of companies have a privileged score, since we assume that a company subject to the standards of the Stock Commission has better corporate governance.

<!-- Start of picture text --> Legal Soundness LEGAL SOUNDNESS<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: LAC SOEs Governance Database, The World Bank, 2009. T1 T2 T3<br>THRESHOLD LEVELS<br>NUMBER OF COMPANIES<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>T1 ECU D R JAM PER ARG COL BZL PAR T2 BOL C R T T MEX URU<br><!-- End of picture text --> The Legal Soundness Index reflects the relatively good position of the majority of companies of the region. Most of government corporations have been corporatized and their functioning subject to commercial law. On the other hand, there is only one company that has been listed. SABESP in Brazil is listed both in domestic and international stock markets.

# **COMPONENT 2: BOARD AND CEO**

Our measure of the governance levels of the Board of Directors and the CEO attempts to capture the quality of both the mechanisms to appoint as well as the qualifications of the members of the Board and the CEO. It prioritizes a Board of Directors where political discretion is low, where members of the Board are selected on pre-defined criteria (particularly related to merits and experience), and whose performance is assessed based on different governance arrangements. The more the emphasis on transparency and accountability of the decision-making authorities of a SOE, the higher will be the possibilities of improving performance.

Governance levels are higher at the level of CEOs than at the level of the Board. Moreover, our measure of CEO’s governance shows one of the few occasions where companies are at T1 levels. Three companies of our sample (EMELNORTE, ELECTROHUILA, and QUINDIO) fit in T1 standards. All of them belong to the electricity sector. Generally, they are appointed by the Board of Directors, are hired under private sector rules, and their performance monitored according to different mechanisms of evaluation.

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related school closures. These data are available for 93% of the sample in ECA+ countries and 98% of the sample in EU countries. We also merge PIRLS data with information from UNESCO on the total number of weeks schools were closed fully or partially during the pandemic. These data are available for all EU countries, although they do not distinguish between primary and secondary levels. Thus, the UNESCO information might be misleading, as primary schools remained open in some countries while secondary schools were closed.

# **Empirical Strategy**

We model the effect of school closures on student achievement by predicting the deviation of the most recent 2021 results from an estimated linear trend in reading achievement using data from all PIRLS rounds. We estimated each country’s linear trend separately and included country-level fixed effects to control for unobserved time-invariant country characteristics. More formally, we estimate a linear regression model on pooled PIRLS data from different years, given by the following equation: In general, PIRLS surveys 4<sup>th</sup> -grade students who are around ten years old at the assessment time. However, students might take the test earlier or later. Some are repeating, and some are advancing a grade. Also, some countries decided to test students in 3<sup>rd</sup> , 5<sup>th,</sup> or even 6<sup>th</sup> grade. Moreover, while most countries sampled the same population over the years, some changed the target population. Finally, due to the pandemic, some countries postponed the assessment to the beginning of the fifth grade. To consider these differences, we took two approaches. In one, we additionally control for student age (10 dummies, one for each age decile, to allow for a non-linear age effect), student grade, and for the average age effect calculated as a mean age for each round and country. In the second approach, we re-estimated all regressions for countries from Wave 1 in PIRLS. These countries did not postpone the assessment due to the pandemic, and according to PIRLS organizers, their data are fully comparable with previous rounds without additional adjustments.

The pandemic could affect student results differently, not only through school closures. Disruptions could also affect health, economic and psychological situation, and general wellbeing. We distinguish these effects by comparing students in countries or schools closed for different periods. In this regard, we used information provided by principals on how their schools were affected by the pandemic. The baseline in the model is more than eight weeks of school closure, and we compare how they differed from a linear trend compared to schools that were closed for between 2 and 8 weeks and those affected for less than two weeks or not closed at all. We also used UNESCO-provided data on the number of weeks of school closures for 4
Ebola impact these age groups differently: younger girls are relatively more impacted by school closures and barriers to re-enrolling, while the older cohort are more impacted by the disruption to economic and health facilities; (iii) an informal institution in rural Sierra Leone is gender specific ‘secret societies’: the role of the women’s society is to initiate girls into adulthood, and there is a traditional separation of older and younger women in these institutions [Bledsoe 1990].

Our main results are as follows. On the impacts of the intensity of the Ebola shock in control villages (so absent ELA clubs), there are two key dimensions along which the economic lives of young women are impacted: economic activities engaged in, and time spent with men.

On the first dimension, we find that residing in a high disruption village leads younger girls to stay out of schools post crisis, i.e. long after schools have reopened and when our endline survey is conducted. This is a large extensive margin response: enrolment rates fall by 16pp in more disrupted villages, or 32% of the baseline mean. The result indicates that in high disruption villages there are persistent impacts of the shock so that it is harder for young girls to re-enrol even long after the policy of school closures ends. Their reallocation of activities is largely towards engagement in income generation: this rises by 19 _._ 1pp (or 238% of the baseline mean). This accelerated school-to-work transition is far higher than what we would have predicted at baseline as these girls become older.

On the second dimension, residing in a high disruption village absent ELA significantly increases the time young women spend with men. The magnitude of the impact is 1 _._ 27hrs/wk, or a near 48% increase over the baseline mean. As a consequence of this increased exposure to men, we see subsequent impacts on pregnancy and risky behaviors. Moving from a low to a high disruption village is associated with a 10 _._ 7pp increase in the likelihood of becoming pregnant, a doubling from baseline. This is nearly all driven by out-of-wedlock pregnancies. These impacts occur at a time when health service provision has been significantly curtailed, and the dangers during childbirth to girls are likely to be even more severe than in normal times.

Our core results examine how the ELA program mitigates these two dimensions of impact.

First, the availability of ELA clubs counters the school-to-work transition among younger girls. More precisely, in high disruption villages randomly assigned to the ELA intervention, the 16pp fall in full time school enrolment is halved (to a 8 _._ 1pp fall). In those locations, the ELA clubs cause girls exclusive engagement in income generation to rise by only 5 _._ 8pp (compared to the 19 _._ 1pp rise absent ELA clubs), and ELA clubs also enable girls to be able to engage in both school and work activities simultaneously. We then show that by reversing the school-to-work transition caused by the aggregate shock, ELA clubs aid the accumulation of basic skills of numeracy and literacy for younger girls, especially in high disruption locations.

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between 1996/97 and 2002/03 are different in different regions, leading to an increased spatial disparity in 2002/03, both the revised and official PLs for 2002/03 and 2008/09 show a similar geographical pattern of growth (see Figure 4, in Appendix) and a high and significant rank correlation (0.74).

As is extensively discussed in the most recent NPA, the pattern of price increases observed between 2002/2003 and 2008/09 helps to explain regional poverty dynamics over the period (DNEAP, 2010). Notably, sharp price increases combined with stagnant agricultural productivity contributed to the bad poverty outcomes of the Central provinces. Likewise, according to the NPA the relatively slower growth of PLs in urban areas nationwide triggered a rapid reduction in observed poverty; this effect was consistent throughout the country (see Figure 4 and the last column of Table 7, both in Appendix).

To further analyze the robustness of the revised PLs vis-à-vis the official figures we constructed a correlation matrix showing poverty rates and consumption proxies for 2002/03 and 2008/09 across the 13 regions (see Table 5 in Appendix). Using the revised PLs, the 13 regional poverty-rate rankings appear more consistent with what other poverty correlates would suggest. These correlations are both negative and statistically significant for proxies associated with lower poverty (e.g. a higher wealth index, better access to services, a higher head-of-household education level), and both positive and statistically significant for proxies associated with poverty (e.g. a higher percentage of stunted and underweight children). By contrast, the official poverty rates show a non-significant rank correlation with all other poverty indicators, raising further doubts about their validity.

In addition, a standard consumption-prediction model is presented in Table 6 (see Appendix) and can be expressed as:

# **Equation 7**

where yi refers to total official household consumption expenditures per capita per day, with zk representing the two different groups of PLs, and subscripts i and j referring to households and locations, respectively.

Since the scale of the dependent variable differs slightly in the two models due to the different PLs used (see Table 3), the simple difference in the t-statistics for each parameter is reported instead of the statistical difference in the coefficient usually reported in the seemingly unrelated regression equation (SURE) models. A higher absolute difference for each parameter can be interpreted as a better capacity of the ratio between consumption expenditures and PLs to be explained by conventional poverty correlates. The results indicate that the model using revised PLs (B) better fits the standard poverty predictions. The adjusted R-square of this model is 22 percent higher than when the official PLs are used. Moreover, the difference in t statistics for the variables associated with poverty (e.g. assets and access to services) are all positive and relatively high, with the only exception being the variable 21
# **3. Data and Methodology**

# **(1) Data**

In light of the significant relevancy of aid effectiveness issues in FCS relative to non-FCS, a critical question is whether aid is more effective in FCS than non-FCS. In order to measure _relative_ aid effectiveness performance in FCS vis-à-vis non-FCS, this paper uses the results of the PD survey due to its three advantages.

First, it is the most comprehensive survey on aid effectiveness, with 15 indicators (including subindicators) covering the five aid effectiveness principles agreed in the Paris Declaration<sup>6</sup> , namely ownership, alignment, harmonization, managing for results, and mutual accountability<sup>7</sup> . Second, it has had a significant and increasing participation from both partner countries and development partners. The number of participating countries increased from 33 in the 2006 baseline survey to 55 in the 2008 survey and 78 in the 2011 survey<sup>8</sup> . More than 50 development partner participated in the 2011 survey. The 2011 survey captured US$70 billion of aid which was about three quarters of the core aid provided to developing countries worldwide (OECD, 2011, p.18). Third, it was conducted three times, in 2006 (for 2005 baseline data); 2008 (for 2007 data); and 2011 (for 2010 data). Three surveys allow us to conduct a comparison between 2005 and 2010 (although each round of the survey had different country coverage).

Among the 15 indicators, 10 indicators are relevant for the analysis of this paper, as they measure the management and delivery of aid while the remaining 5 measure quality of country systems etc. (Table 1).

**Table 1: Ten PD Survey Indicators**

|**Principle**|**Indicators**|**How Measured**|
|---|---|---|
|Alignment|**Ind 3:**Aid flows are aligned with national<br>priorities (aid on budget)|Aid flows reported in the annual budget by<br>government/aid<br>flows<br>actually<br>disbursed<br>by<br>development partners (%)|
|Alignment|**Ind 4:**Strengthen capacity by coordinated<br>support (technicalassistance)|Amount of coordinated technical cooperation /<br>totaltechnicalcooperation(%)|
|Alignment|**Ind 5a: **Use ofcountryPFMsystems|Use of PFM/totalassistance (%)|
|Alignment|**Ind 5b:**Use of country procurement<br>systems|Use of procurement / total assistance (%)|
|Alignment|**Ind 6:**Strengthen capacity by avoiding<br>parallel<br>project<br>implementation<br>units<br>(PIUs)|Number of parallel PIUs|
|Alignment|**Ind 7:**Aid is more predictable|Actual<br>aid<br>flows<br>recorded<br>by<br>the<br>government/scheduled aid flows by development<br>partners (%)|
|Alignment|**Ind8:**Aid is untied|OECD-DAC data|
|Harmonization|**Ind 9:**Use of common arrangements or<br>procedures (program-based approaches—<br>PBAs)|Program-based approaches (budget support + non-<br>budget support PBAs)/total assistance (%)|

> 6 <u>The Paris Declaration on Aid Effectiveness</u>

> 7 Appendix 1 has list and brief explanations of 15 indicators.

> 8 Seventy-eight countries include both Sudan and South-Sudan.  As Sudan data includes South-Sudan, this paper used data of 77 countries excluding South Sudan in the 2011 survey.

5