rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4777

# **Abstract**

This paper presents findings from baseline surveys on student learning achievement, teacher effort and community participation in three Indian states, Karnataka, Madhya Pradesh and Uttar Pradesh. Results indicate low teacher attendance and poor student learning. Parents and school committees are neither aware of their oversight roles nor participating in school management. However, there is substantial heterogeneity in outcomes across states. Karnataka has better student and teacher outcomes as well as higher levels of community awareness and participation than the other two states. The authors find substantial variation in teacher effort within schools, but most observable teacher characteristics are not associated with teacher effort. One reason for low teacher effort may be lack of accountability. Regression analysis suggests low rates of teacher attendance are only part of the problem of low student achievement. The gains in test scores associated with higher rates of attendance and engagement in teaching are small in the states of Madhya Pradesh and Uttar Pradesh, suggesting teachers themselves may not be effective. Ineffective teaching may result from lack of accountability as well as poor professional development of teachers. Further research is needed to examine not only issues of accountability but also professional development of teachers.

This paper—a product of the Human Development Department, South Asia Region—is part of a larger effort in the department to promote rigorous studies which can inform policy. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ppandey@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
Finally, expanding infrastructure investment in EMDEs has gained an unprecedented urgency in view of _climate change_ , and the fact that in the coming decades all GHG net emissions additions will originate in these countries, and they will also suffer most of the effects. The demand for both mitigation and adaptation investment will see an extraordinarily large increase, and within mitigation, energy transition looms dominant.<sup>9</sup> In this context, private finance has assumed a paramount role, and new instruments and mechanisms to attract private investment – both in terms of financial flows and direct investment - as proposed here, are likely to be regarded of first order importance to address climate change and to pursue the “net zero emission by 2050” target.

other instruments, so as to minimize the risk of expropriation and other forms of opportunistic behavior, and further suggest they are generally incentive-efficient, and could thus be construed as mechanisms to reduce the risk of renegotiation.

> 9See, for instance, International Monetary Fund (2022). Songwe et al. (2022) also states that "The world needs a breakthrough and a new roadmap on climate finance that can mobilize the $1 trillion per year in external finance that will be needed by 2030 for emerging markets and developing countries (EMDCs) other than China” with the private sector making the largest increase in financing, both foreign and domestic. In addition, a major recent statement and analysis is found in World Bank (2023).

11
**Table 18: Findings of Selected Country Studies**

|**Country**|**Study**|**Methodology**|**Results**|
|---|---|---|---|
|Canada|Ridding, Madill and<br>Haines (2007)|Propensity score matching|75% of guarantee users would<br>not have been able to get a loan<br>without theguarantee|
|Chile|Larrain and Quiroz (2006)|Quasi-Natural experiment<br>(participation of banks at different<br>point of time)|The scheme increases the<br>probability of small firms to get<br>a loan by14%|
|France|Lelarge, Sraer, Thesmar<br>(2008)|Quasi-Natural experiment<br>(extension of theguarantee scheme)|Significant additionality|
|Korea|Oh, Lee, Choi and<br>Heshmati (2006)|Propensity score matching|Significant additionality|
|Malaysia|Boocock, Sharif (2005)|Interviews of guarantee users|Additionality|
|UK|Cowling (2010)|Propensity score matching|Significant additionality|
|US|GAO (2007)|Descriptive statistics (comparison<br>of the credit score of guarantee<br>users to the score of non-guarantee<br>users)|Additional measures are needed<br>to evaluate additionality|

# _Assessing Additionality in MENA_

**Until now, there have been no rigorous evaluations of the additionality of MENA schemes.** Most guarantee schemes in MENA have not yet conducted rigorous impact evaluation studies to examine their degree of additionality and economic impact.  This is partly due to the fact that some of these schemes are not sufficiently mature, but this is a deficiency that should be addressed in the near future, as these evaluations can provide substantive insights for improvements in the design and effectiveness of the scheme.

# _5.3 Financial Sustainability_

_Measurement and International Experience_

**The financial sustainability of a guarantee scheme refers to its capacity to contain losses and maintain an adequate equity base vis-a-vis its expected liabilities** .  One of the basic indicators used to assess the financial sustainability of a guarantee scheme is the equity ratio (the ratio of equity to outstanding guarantees) or inversely the multiplier (the ratio of outstanding guarantees to equity).<sup>14</sup> A sound guarantee scheme should maintain the multiplier below a certain threshold, which would depend on the risk of the portfolio.  When the multiplier exceeds the target value, the guarantee scheme should take actions to reduce its costs, increase its revenues, or get additional funding.  Multipliers of guarantee schemes vary widely from 3 to 20, reflecting government policies, risk management, and also the maturity of the scheme (Figure 7). Some guarantee schemes use counter-guarantees, allowing them to increase their leverage.

**Sound rules and effective risk management are essential to contain losses and preserve the equity base** . Although 60% of guarantee schemes around the world are not profitoriented (Beck et al, 2008), containing losses is key to ensuring financial sustainability.  As

> 14 The equity ratio is roughly equivalent to the solvency ratio in the insurance sector.

30
data on GDP growth, CPI inflation, the lending rate, LCU/EUR exchange rate change, and non-performing loans as a proportion of total loans (NPLs). We work with fourth differences of the variables, i.e., four-quarter or year-on-year GDP growth, CPI inflation etc., measured in percentages.

**Table 3:** TTC, Baseline PIT and Stress Scenarios.

|Macroeconomic Scenarios|TTC||Fut|ure (_t_+1)||
|---|---|---|---|---|---|
|||PIT|Stress VAR|Stress Int.|Pr(Stress Int.)|
|NPL ratio (%)|10.1%|10.1%|12.0%|22.9%|_NA_|
|GDP growth (% change)|3.2%|0.5%|_−_6.9%|_−_6.3%|86.7%|
|Infation (% change)|2.8%|2.4%|11.7%|26.5%|99.8%|
|Real interest rate (%)|9.4%|9.3%|10.0%|19.0%|100.0%|
|Exchange rate (LCU/EUR) (% change)|0.0%|0.0%|0.0%|_−_31.5%|99.7%|

**Notes:** A negative value ( _−_ ) in the (nominal) exchange rate denotes a depreciation of the Local currency unit (LCU) relative to the EUR.

The TTC values are the long-run equilibrium values of the four macroeconomic variables of interest. We compute these equilibrium values as simple arithmetic averages us- _−_ ing quarterly data over the period from 1996:Q1 2012:Q4. In addition, we use two yearahead consensus forecasts to increase the effective sample size when calculating these avValues used in the baseline PIT scenario are the market consensus forecasts.<sup>39</sup> The erages. data for both the TTC and PIT scenarios were obtained from the Economic Intelligence Unit.

The country specific scenario labeled as “Stress VAR” in Table 3 is the four step-ahead dynamic forecast of the macroeconomic variables from a VAR(1) model. The stress values correspond to the adverse 1% tail values of the respective forecast distributions. The _−_ VAR(1) model was estimated on country specific data covering the period from 1996:Q1 2010:Q4, and standard lag length selection criteria were used to determine the appropri-

> 39One could also use the four step-ahead point forecasts from the VAR model.

50
<!-- Start of picture text --> FIGURE III.2<br>ALTERNATIVE MEASURES OF FOREIGN PRICES<br>(In U.S.Dollars.Base 1975.1=1)<br>1.91<br>0.9<br>i CPI's Basket + Price of Tra~de<br>FIGURE 111 . 3<br>;ALTENTIYIE REALEXCHN1GE RATES<br>1133<br>1975.1 1977.1 197.1 198i.1 1983.i 1985.1 1987.4<br>ALERAIVE REAL EXCH.AHGE RAPTES<br>(Bs34eM<br><!-- End of picture text -->
||`(15.89)***`|
|---|---|
|`ln(historical booms)`|`-0.070`|
||`(1.52)`|
|`ln(GDPper capita)`|`3.028`|
||`(3.27)***`|
|`ln(GDPper capita)`<sup>`2`</sup>|`-0.163`|
||`(3.20)***`|
|`ln(population)`|`0.139`|
||`(5.81)***`|
|`commodity group 2`|`0.182`|
||`(1.06)`|
|`commodity group 4`|`0.213`|
||`(1.42)`|
|`commodity group 5`|`0.497`|
||`(3.10)**`*|
|`commodity group 6`|`0.726`|
||`(4.10)**`*|
|`commodity group 7`|`1.338`|
||`(9.59)**`*|
|`commodity group 8`|`1.025`|
||`(7.23)**`*|
|`commodity group 9`|`0.800`|
||`(5.65)**`*|
|`commodity group 10`|`1.333`|
||`(9.40)***`|
|`Constant`|`-13.834`|
||`(3.19)***`|
|`Observations`|`385`|

``` Absolute value of z statistics in parentheses
* significant at 10%; ** at 5%; * at 1%
```

# **Data Definitions and Sources**

|**Variable**<br>**Name**|**Description**|**Units**|**Year(s) Used**|**Transforma**<br>**tion**|**Source**|
|---|---|---|---|---|---|
|Patents|Patents granted by<br>the USPTO|Sum of<br>Counts in<br>the period|1963-1981<br>(historical)<br>1982-1986<br>(period 2)<br>1987-1991<br>(period 3)<br>1992-1996<br>(period 4)<br>1997-2001<br>(period 5)|None|Lederman and<br>Saenz (2003)|
|Discoveries|Counts of discoveries<br>in the<br>country/commodity<br>group|Counts|1997-2002|None|Klinger and<br>Lederman (2005)|
|Export<br>Booms|Counts of export<br>booms (products with<br>growth greater than<br>10%in at least6|Counts|1997-2003|None|COMTRADE|

26
<u>2012). The criterion function used to select the probabilities corresponding to the coefficients of</u> the APC model is the entropy measure by Shannon (1948) that is identified by requirements that any measure of uncertainty should be continuous, symmetric with respect to reordering of the outcomes, and additively decomposable, and bounded (which is the case for most demographic and economic data). An appealing feature of the APC ME method is that it overcomes the potential arbitrariness of identification restrictions. We use the ME estimator, but we compare our results to the estimates based on the approach by Deaton and Paxson (1994).<sup>11</sup> We model the job tenure _Tiapc_ of individual _i_ as a linear function of the individual’s age ( _ai_ ), period ( _pi_ ), and cohort ( _ci_ ) effects. We define a (the average tenure of individuals who were age _a_ in survey year _p_ and cohort _c_ ) such that aTT =aa ∑inni1 ii i <u>i</u> ~~,~~ where a is the number of individuals in i i= TT the corresponding APC cell. We also define vectors of age, period, and cohort dummies: TT aa nn ii nn aa = , , and , where _K_ kk is AA the number of age categories, ll _L_ is the number of survey rounds, mm _M_ is the number of generational ll[kk= aa], kk= 1, ... , KK PP = ll[ll= pp], ll= 1, ... , LL CC = ll[mm= cc], mm= 1, ... , MM cohorts, and is an indicator function. Then, ll a = ∑ + ∑ + ∑ a , (1) KK LL MM aa kk=1 kk kk ll=1 ll ll mm=1 mm mm aa where a denotes the error term. TT αα AA ππ PP γγ CC + εε aa εε To estimate model (1) using the APC method, we construct a panel of individuals by their APC identifiers. The EU-LFS collects age information in five-year intervals, and we combine survey years and cohorts to correspond to these five-year intervals to preserve the APC relationship. Thus, we have 11 age intervals, 5 survey-year intervals, and 15 age cohorts.

<u>Figure 3</u> shows the age effects associated with both estimation methods. Panel a presents the changes in average tenure along the life cycle of workers estimated by the ME method (Browning, <u>Crawford, and. Knoef 2012). Panel b shows the result of the Deaton-Paxon (DP) (1994)</u> decomposition. As expected, tenure increases with age almost linearly, with a sign of stabilization after 55. The ME decomposition estimates that, on average, a 30-year-old worker has 3 years more

> 11 The least squares estimator proposed by Deaton and Paxson (1994) remains popular and widely used in economic analysis, despite its deficiencies.<sup>12</sup> Ten years is about the average job tenure for the workers in our sample. Gregg and <u>Wadsworth (2002) used similar tenure categories in analyzing the evolution of job tenure in Great Britain between</u> 1975 and 2000.

12
|Table|13: Cashew Nut Suppliers to T<br>(1992/93 Season)|CMB Lindi|
|---|---|---|
|First Handler/Supplier|Quantity Supplied|Proportion|
|Coop. Society/Union|293.6|6.5%|
|R.T.C. (Public Enterprise)|202.5|4.5%|
|Private Traders<br>Of which:<br>Tanzanian Asian (14)<br>Indigenous (17)|3988.2<br>2048.6<br>1939.6|89.0%<br>45.7%<br>43.3%|

# Sources: TCMB Lindi Buying Records; Lindi Regional Agricultural Staff.

The pattern for private exporters is quite different. During the 1992/93 season, twelve private companies undertook (or were preparing) exports, accounting for about 40% of the country's raw nut trade. Nine of these exporters were interviewed, basic information on which is provided in Table 14 below. By Tanzanian standards, each of these firms is quite large or is part of a larger group of companies. In contrast with the mixed pattern found for local buyers/traders, each of the exporters is owned and/or managed by Tanzanian Asians, typically family-based companies or partnerships. As the Table indicates, only one firm concentrates on cashew nut trading, but even this firm is part of a conglomerate. Each of the other firms is active in trading of a wide range of commodities, plus most are also active in the transport business and in agro-processing ventures. During the 1992/93 season, cashew nut trading accounted for 20% or less of the turnover of most of these firms. These firms are also among the country's largest traders of non-traditional agricultural commodities, including sesame, cassava roots, and others.

Table 15 outlines the trade mix of one of the largest emergent cashew nut exporters. Notice both the extensive range of products and the diversity of markets served. These new cashew nut exporters are generally quite experienced in international trade or else have hired experienced managers from abroad to develop this business. Several of the new exporters had experience in cashew nut trading during the 1960s and 1970s.

While several of the firms financed their cashew nut purchases out of their own funds, most obtained some additional funding, either from local commercial banks or from external buyers or affiliated companies. Such external finance provided a strong competitive edge, as money was available on a timely basis and was available at interest rates below 10%, compared with the 27-31% interest rates on local overdraft facilities.

As noted above, the private exporters are generally not directly involved in the local-level procurement of the crop. They instead have relied upon private traders (or less commonly cooperatives) based in the main production areas to arrangement crop collection, grading, farmer payments, and the preparation of consignments for shipment. The buying agents are not strangers; several are linked through family, while others have previously dealt with the exporter with other commodities (e.g. sesame, cassava). Trust is essential in this type of trade, given the relatively large sums of money which the exporter provides the agent to make purchases, and given the fact that the exporter is simultaneously negotiating forward export contracts for which the agent's supplies will help fulfill.

31
with respect to national GDP (see Annex 1) which suggest that federal transfers could be amplifying the pro-cyclical behavior of state expenditures.<sup>6</sup> Consequently, the Brazilian federal government does not appear to play a (macroeconomic) stabilization role with regard to the states, particularly in the context of symmetric shocks to the regions. By design, most intergovernmental transfers in Brazil are automatic, without a stabilizing component.<sup>7</sup> Table 3: Cyclical Sensitivity of Fiscal Policy: Subnational Level

||||||Feasible<br>||
|---|---|---|---|---|---|---|
|Variables|OLS||Fixed<br>Effects(FE)||Generalized Least<br>Squares(FGLS)||
|**_Revenues:_**|||||||
|Total Revenue|0.190|**|0.197|**|0.273|***|
||[0.082]||[0.093]||[0.036]||
|Intergovernmental Transfers|-0.003||0.034||0.091||
||[0.123]||[0.146]||[0.066]||
|Tax Revenue|0.303|***|0.274|***|0.309|***|
||[0.071]||[0.075]||[0.030]||
|V.A.T. (ICMS)|0.266|***|0.237|***|0.312|***|
||[0.074]||[0.081]||[0.040]||
|**_Expenditures:_**|||||||
|Total Expenditure|0.377|***|0.397|***|0.305|***|
||[0.116]||[0.128]||[0.031]||
|Primary Expenditure|0.358|***|0.376|***|0.302|***|
||[0.118]||[0.129]||[0.073]||
|Maintenance and capital|0.468|***|0.503|***|0.340|***|
||[0.142]||[0.153]||[0.089]||
|Personnel Expenditure|0.823|***|0.859|***|0.703|***|
||[0.227]||[0.250]||[0.057]||
|Interest Payments|0.538|*|0.551||0.516|***|
|***, **, and * denote significance|[0.314]<br>at the 1%, 5|%, and|[0.352]<br>10% levels, res|pectiv|[0.127]<br>ely. Standard Errors||

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Standard Errors in brackets. Robust standard errors in the OLS and FE estimation. Correction for heteroskedasticiy and autocorrelation (AR(1)) in the FGLS estimation.

6 As mentioned by Wibbels and Rodden (2006, p.9), there is evidence that national transfers in federal systems may exacerbate the procyclicality of provincial revenues. Sorensen, Wu, and Yosha (2001) show that grants from the U.S. federal government to the states are positively correlated with the business cycle. In addition, Seitz (2000), and von Hagen and Hepp (2002) show that revenues flowing to the German states through its tax-sharing scheme are procyclical.

7 At the federal-state level the main transfer program is the State Participation Fund (FPE), and the states’ share of the taxes that feed the Fund is based on fixed percentages of particular taxes.

10
Paraguay, and El Salvador. Surveys were processed following the protocol of the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a joint project between CEDLAS at the Universidad Nacional de La Plata and the World Bank.<sup>3</sup> Household surveys are not uniform across Latin American countries and in most cases not even within a country over time. The issue of comparability is of great concern. Owing to that situation, we make all efforts to make statistics comparable across countries by using similar definitions of variables in each country and by applying consistent methods of processing the data (SEDLAC, 2021).

For each country in our sample, we pool the available survey waves from 2010 (2009 in some countries) to 2020. The final sample includes children and young people in the age range from 6 to 24. To estimate the change in enrollment in 2020, we run a regression of the outcome of interest (whether the child is enrolled in education or not) on a linear time-trend and a dummy variable indicating that the observation refers to the year 2020.<sup>4</sup> Several control variables are also included to abstract from potential changes in sample composition, namely age, sex, urban or rural area of residency, and parental education. Formally, we estimate the following equation: where _Y_ indicates whether individual _i_ surveyed in year _t_ is enrolled in education (or in private education), _A2020_ is a dummy variable that takes the value 1 for the year 2020, τ stands for a linear time trend, _X_ is a vector including the control variables mentioned above and ε is the error term. The coefficient of interest is β. It indicates the deviation, in percentage points, of the year 2020 with respect to the overall trend in enrollment rates, net enrollment rates, or enrollment in private education, conditional on all the above-mentioned controls. Enrollment

> 3 Table A.1 in the Appendix shows basic information on the Latin American national household surveys used in this paper.

> 4 For simplicity, we use the terms enrollment and attendance interchangeably. The typical question included in household surveys asks whether the child is attending school, understood as whether the child is participating in the educational process (either in presence, during a normal year of schooling, or through remote learning during school closures in the year of the pandemic).

6
38

||**Appendix A: C**<br>**End of Conflict**|**ountr**|**y and**|**Period**|**Covera**|**ge (con**|**t.)**||
|---|---|---|---|---|---|---|---|---|
||**Year(s)**|**1970-74**|**1975-79**|**1980-84**<br>|**1985-89**<br>|**1990-94**<br>|**1995-99**<br>|**2000-04**<br>|
|Nicaragua|1979, 1990|||√|√<br>|√<br>|√<br>|√<br>|
|Niger|||||√<br>|√<br>|√<br>|√<br>|
|Nigeria|1970, 1984|√<br>|√<br>|√<br>|√<br>|√<br>|√<br>|√<br>|
|Norway||√|√|√|√|√|√|√|
|Pakistan|1971, 1977, 2002|√|√|√|√|√|√|√|
|Panama||||√|√|√|√|√|
|Papua New Guinea|1998||√|√|√|√|√||
|Paraguay||√|√|√|√|√|√|√|
|Peru|1996|√|√|√|√|√|√|√|
|Philippines||√|√|√|√|√|√|√|
|Rwanda|1964, 1994, 2001||||||||
|Senegal|1999|√|√|√|√<br>|√<br>|√<br>|√<br>|
|Sierra Leone|2001||||√|√|√|√|
|South Africa|1994||||||||
|Sri Lanka|1971, 2002|√|√|√|√|√|√|√|
|Sudan|1972, 2002|√|√|√|√|√|√|√|
|Sweden||||√|√|√|√|√|
|Switzerland||√|√|√|√|√|√|√|
|Syria|1982|√|√|√|√||||
|Thailand|1982|√|√|√|√|√|√|√|
|Togo|||√|√|√|√|√|√|
|Trinidad & Tobago|||√|√<br>|√<br>|√<br>|√<br>|√<br>|
|Tunisia||||√|√|√|√|√|
|Turkey|1999|√|√|√|√|√|√|√|
|Uganda|1966, 1979, 1987, 1992||||√|√|√|√|
|United States||√|√|√|√|√|√|√|
|Uruguay||√|√|√|√|√|√|√|
|Venezuela||√|√|√|√|√|√|√|
|Zambia|||||√|√|√|√|
|Zimbabwe|1979,1987||√|√|√|√|√|√|

Note: End of conflict years are only reported for those countries for which we have RER misalignment data in the period 1960-04. These are used for the analysis in sections1-4 of the paper. Checkmarks indicate that the country-period combination is included in the growth regressions.