rafmacalaba/datause-encoder-data huggingface dataset:
was provided to 12 percent of patients) and the complete lack of correlation between the quality of care and the availability of equipment and medicines or patient-load. Further, the study also confirmed the earlier DH results that quality in the public sector was _worse_ than among completely untrained private sector providers. See Das et al (2012).

Despite the official clearance from IRBs, again, there are tricky issues that arise in the use of informed consent and the nature of the study. Had the researchers been forced to inform providers of the arrival of SPs, detection rates would have been high in the rural context and the study could not have been completed. In the course of the study, careful safeguards ensured that all data were anonymous _and_ that no provider even knew that they had participated in such a study. Therefore, it is difficult to argue that there was any harm to either patients or providers through the course of the study, but a strict interpretation of informed consent “at all costs” was not possible in this context.

This issue relates to a wider debate over the use of stealth surveys by anonymous observers sent to ascertain the quality of services.<sup>6</sup> Other examples from the literature pertain to the use of false shoppers or patients who observe the activities of a doctor, teacher, or other agent but who do not seek their consent or inform them directly of the purpose of the visit. In such situations a researcher is presumably seeking an assessment of average quality of services and not that of a specific individual. Thus, the information is expected to remain anonymous and not to be used to punish the individual or seek damages. In partial contrast, employers may use stealth clients to monitor performance usually with the employee informed that they may be observed in this manner. Researchers are sometimes advised to use a similar strategy of informing potential subjects (participants seem an inappropriate label in this circumstance) that

> 6 See a blog by Jed Friedman on this subject, http://blogs.worldbank.org/impactevaluations/sometimes-it-is-ethicalto-lie-to-your-study-subjects

21
# **1 Introduction**

Although the cost effectiveness of investing in early childhood is widely recognized, only 20% of children living in low-income countries were enrolled in preprimary education in 2019. In Mozambique, the situation is even more disturbing, with only 3.5% of children between 3 and 5 years of age enrolled in preschool (MINEDH, 2020).<sup>1</sup> In a country with one of the highest levels of poverty in the world, low preschool attendance is a huge missed opportunity. A growing body of evidence finds that interventions targeted at children from birth to six years of age have great promise in improving children’s lifetime path of human capital and income (Holla et al., 2021; Black et al., 2017; Almond and Currie, 2011; Knudsen et al., 2006; Bos et al., 2024; Attanasio et al., 2022; Bailey et al., 2021; Sylvia et al., 2021; Attanasio et al., 2014).<sup>2</sup> Furthermore, evidence shows that preschool programs in low- and middle-income countries can yield benefit-to-cost ratios ranging between 3.5 and 103.5 (Holla et al., 2021) and that increasing preschool enrollment rates in these countries to 25% could yield an estimated US$10.6 billion through higher educational achievement (Engle et al., 2011). However, designing scalable preschool policies in environments of extreme deprivation has proven challenging due to—among other reasons—inadequate implementation capacity (Bernal et al., 2019; Bouguen et al., 2018; Berkes et al., 2024).

In this paper, we use a cluster randomized control trial (RCT) to evaluate the implementation of a large-scale preschool construction program in Mozambique—the Mozambique Early Childhood Development Project (henceforth DICIPE, from its Portuguese acronym). The program targeted children aged 3 to 5 years living in rural communities in Mozambique and involved unique characteristics that resulted in increased pri-

> 1The small proportion of children who benefit from preschool enrollment does so mainly through private or community schools (MINEDH, 2020).

> 2In a recent meta-analysis, Holla et al. (2021) estimates that preschool education interventions improve children’s cognitive and socioemotional skills by an average of 0.15 SD and 0.12 SD, respectively, during the preprimary period. The authors also estimate that in the post-preprimary period, children benefiting from preschool interventions show a significant advantage of 0.07 SD and 0.094 SD in cognitive and social-emotional skills, respectively. Furthermore, preschool programs improve the long-term outcomes of beneficiaries, including educational attainment, labor, and crime outcomes.

1
Policy Research Working Paper 6752

# **Abstract**

This paper uses data from a post-hoc evaluation of a performance-based teacher incentive program in the Kyrgyz Republic to examine the opinions of teachers receiving different pay bonuses based on their performance as assessed by external evaluators. Overall, teacher opinions of the program were favorable, although teachers who received lower performance ratings held less favorable opinions about the motivational aspects of the program. Despite this, lower-rated teachers were more likely to report that they used what they learned to evaluate their own teaching, as compared with more highly rated teachers, and were more likely to take professional development courses in the years following the program’s implementation.

This paper is a product of the Education Unit, Europe and Central Asia Region. 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://econ.worldbank.org. The author may be contacted at mlockheed@verizon.net.

_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
- 49 -

banks experienced severe liquidity problems. Three were dissolved, while another was acquired by a stronger bank. By the end of 1994, the total open position of the banking sector had narrowed to US$860 million, but it has since expanded again close to US$5 billion by the end of 1997.

**Table 26 TURKEY**

|**Balance Sheet O**|**pen Short Foreign Exc**<br>|**hange Positions of Banks**<br>|
|---|---|---|
||US$ billions|Percentage of Capital|
|1992|2.9|48|
|1993|4.6|68|
|1994|0.8|18|
|1995|3.1|51|
|1996|2.5|34|
|1997|4.9|55|

Source: The Banks Association of Turkey.

There is a systemic aspect to this risk. Virtually all of the risk-taking banks are positioned in the same direction, holding short foreign currency positions and borrowing short and lending/investing long. Moreover, these banks are the dominant players in both currency and government securities markets. Therefore, if there is a strong rate movement against either or both of these risk positions, the banking sector may move in unison to shed it holdings, pushing the lira down and interest rates up, and leaving the Central Bank as the sole stabilizing force in the market. These factors suggest that the Central Bank may be constrained in its policy options for crisis management. If, for example, it wishes to defend the currency by raising interest rates sharply, such action would have serious consequences for the banks because of their interest rate risk positions.

**Quality of Loan Assets** One of the striking features of Turkey's private-sector banks is the low ratio of loans to total assets, and the short-term nature of such loans. While this fact may cause some concerns, it has a beneficial liquidity effect - namely that a greater proportion of assets can be held in more liquid asset-types, such as securities, and the loans can be called and turned into more liquid assets more easily, The securities can provide liquidity via sale, repo, or when pledged against interbank borrowings. On the other hand, loans are still the largest asset item, and large credit risks have profound implications for system liquidity.

The level oif credit risk in the Turkish banking system appears to be moderately low as measured by barnks reported level of non-performing assets, and the banks appear to be
faced by the cotton producing countries of West and Central Africa (WCA), namely, the structural inefficiencies of the primary processing industry (also known as ginning), and the appreciation of the CFAf (the common currency of WCA countries) against the $US. Without downplaying the importance of trade interventions, this paper concludes that addressing the technology, inefficiency, and macro impediments should receive high priority on the policy making agenda.

The rest of the paper proceeds as follows. The next section briefly examines the origins and the history of the cotton dispute. Section 3 discusses the broader implications of the agreement between Brazil and the United States. The penultimate section goes beyond trade and subsidies and argues that addressing the development aspects of the cotton dispute is complicated by a number of factors, the most important of which are the structural changes that have taken place in the global cotton market and the domestic inefficiencies faced by WCA cotton producers, including the countries that brought the cotton case to the WTO. The last section concludes.

# **2. THE COTTON DISPUTE**

The origins of the cotton dispute go back to 2002 when Brazil and four African cotton producers (Benin, Burkina Faso, Chad, and Mali, the so-called C-4) argued that cotton subsidies caused world cotton prices to decline and reduced their export revenues.<sup>**1**</sup> At the time, the value of global cotton output averaged between $25 and $30 billion and the United States (which accounts for one-third of world cotton exports) supported its cotton industry to the tune of $2 to $4 billion annually (see figure 1). The EU provided considerable support to its cottons sector as well—around $1 billion annually—though applied to much less cotton and hence much lower impact on world prices.<sup>**2**</sup>

> **1** The issue of cotton subsidies received traction during a conference, co-sponsored by the International Cotton Advisory Committee and the World Bank during July 8-9, 2002, ‚Cotton and Global Trade Negotiations‛ in Washington, DC. The conference attracted a diverse group of participants including representatives from cotton producing countries (both government officials and private sector), civil society organizations, embassies, and international organizations. The ‘cotton problem’ received more publicity after Oxfam (2002) published the report ‚Cultivating Poverty‛ which highlighted both trade and development implications of cotton subsidies. A behind-thescenes account of the cotton dispute can be found in a Harvard Business School case study (see Milligan, Goldberg, and Lawrence 2009).

> **2** Numerous other countries subsidize their cotton sectors as well. However, they have received less attention either because their subsidies are small and indirect (e.g., India, Turkey, and some WCA countries) or because the accuracy of the statistics has been questioned (e.g., China). See ICAC (2010) for the latest update on cotton subsidies.

3
Table D1: Baseline Characteristics of Clinics, by Online Survey Response Status

||Non‐<br>respondent|Respondent|_P_‐value|Obs.|
|---|---|---|---|---|
|Number of Pregnant Women Attended per Year|48.60|64.90|0.33|36|
|Weeks Pregnant at 1<sup>st</sup>Prenatal Visit|17.44|16.77|0.15|36|
|1<sup>st</sup>Visit before Week 13 of Pregnancy|0.34|0.38|0.27|36|
|% of Pregnant Women who are Plan Nacer Beneficiaries|0.61|0.64|0.59|36|
|Tetanus Vaccine During Prenatal Visit|0.74|0.81|0.22|36|
|Number of Prenatal Visits|4.26|4.42|0.72|36|
|Birth Weight (Grams)|3,283|3,320|0.33|36|
|Gestational Age (Weeks)|38.65|38.47|0.57|31|
|Low Birth Weight (< 2500 Grams)|0.06|0.07|0.73|31|
|Premature (Gestational Age < 37 Weeks)|0.10|0.13|0.60|31|

Notes: This table reports the means of baseline characteristics for clinics that responded to the May 2015 online survey and for clinics that did not respond. The characteristics are taken from the medical records information system (2009). The _p_ ‐values for the tests of differences in means are computed using permutation tests that are robust for small sample sizes.

56
“voracity” effect). Talvi and Vegh (2005) argue that political pressures to spend in good times induce policymakers to try to reduce primary surpluses (by cutting tax rates) to limit wasteful spending. Finally, a related explanation is offered by Alesina et al. (2008) who argue that, in a corrupted political environment, voters will attempt to “starve the beast” to prevent corrupt government offi cials from misappropriating funds by demanding more transfers and lower taxes. We test these three theories and find empirical support for the second (Talvi and Vegh) and third (Alesina et al.) but not for Tornell and Lane’s voracity effect. The policy implication is that the fiscal authority is most vulnerable in good times and resisting political pressures to spend should be a top priority.

Having tested the main theories behind mismanaging abundance, we take advantage of our econometric framework to test whether institutional variables can help fiscal authorities resist political pressures to spend. We find that better public institutions (such as government stability and law and order), commodity stabilization funds, and fiscal rules help reduce fiscal procyclicality. We instrument fiscal rules with bureaucracy quality to take care of reverse causality problems.

Our third, and novel, contribution is to quantify the “when it rains, it pours” phenomenon (a term coined by Kaminsky et al., 2004). This expression calls attention to the fact that, by reacting procyclically, policymakers amplify the underlying business cycle. For instance, if a fall in commodity prices provokes an initial recession (the “rain”), cutting government spending in response (i.e., procyclical fiscal policy) will, through the fiscal multiplier, aggravate the initial recession (the “pours”). In other words, the total fall in output is the sum of rain plus pours since the government amplifies the initial recession by adopting a contractionary fiscal policy. Despite its obvious policy importance, there is little empirical evidence on the quantitative importance of this “pours”channel.<sup>6</sup> Commodity exporters should constitute an ideal testing ground because changes in commodity prices lead to large commodity cycles (World Bank, 2022).

To estimate the pours component, we proceed in four steps. In the first step, we estimate the response of real GDP to a country-specific commodity export price index

> 6Notice that procyclicality would not affect output if the fiscal multiplier were zero. Hence, estimating the fiscal multiplier (not an issue that typical papers on fiscal procyclicality are concerned with) is a critical step in our pours analysis.

4
Page 15 BOX: 4 Female Enrollment in India' Primary and Secondary Schools Since independence, female enrollment has accelerated faster than male enrollment, but girls remain at a disadvantage. Gross enrollment ratios for girls in Standards I-V increased from about 25 percent to about 77 percent; for boys, only about 61 percent to 109 percent. For Standards VI-VIII, girls' enrollment rates increased still more dramatically, from 4.6 percent to 38.1 percent, compared to an increase for males from about 21 percent to 65 percent.

Despite this progress, only about 40 percent of students enrolled in primary school today are girls, and only about 35 percent of the middle school students. The 1981 census indicates that the most educationally deprived young children are rural girls, particularly from lower income groups. About half of all children 6 to 13 attend school; only one-third of rural girls in that age group do. To put it another way, half of all children 6 to 13 who are not attending school are rural girls -- some 37 million girls in 1981.

The 1981 census suggests that only about 9 percent of rural girls (compared to 31 percent of rural boys) aged 15-19 still attend school. Only about 3 percent of the girls complete high school or higher secondary school. Many rural Indian girls aged 14-17 are married. The picture in urban areas is more encouraging. Some 34 percent of urban girls in this age group still attend school, compared to 50 percent of boys. In 1980-81, girls constituted some 18 percent of high school and higher secondary students in urban areas and 10 percent in rural areas. The proportion of women to men in higher (university) education increased to 37 percent in 1980-81.

Source: Kurrien, World Bank, 1989b Background Paper 1988
# **Table 8. Cross section 1971-1995, 2SLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.537|-1.983|-2.839|-3.184|-4.132|27.406|
||(1.109)**|(1.245)|(1.341)**|(1.322)**|(3.062)|(93.724)|
|GDPpc71|-0.001|0.000|-0.001|-0.000|-0.001|0.003|
||(0.000)***|(0.000)|(0.000)**|(0.000)**|(0.001)|(0.012)|
|prim_expen_70|-0.063|-0.075|-0.047|-0.047|-0.068|0.107|
||(0.047)|(0.055)|(0.072)|(0.050)|(0.051)|(0.614)|
|second_expen_70|0.002|0.001|-0.003|0.002|0.004|-0.073|
||(0.003)|(0.003)|(0.006)|(0.003)|(0.004)|(0.203)|
|Invt_GDP7195|0.230|0.230|0.248|0.250|0.253|0.245|
||(0.042)***|(0.053)***|(0.055)***|(0.051)***|(0.048)***|(0.416)|
|main7195|0.028||||0.028||
||(0.013)**||||(0.014)**||
|egc7195||-6.453||||-48.742|
|||(5.223)||||(164.983)|
|rail7195|||1.892||||
||||(1.522)||||
|pavroads7195||||0.464|||
|||||(0.429)|||
|tel_faults7195|||||0.012||
||||||(0.022)||
|elec_loss7195||||||-1.881|
|||||||(6.248)|
|Observations|44|44|36|41|41|30|
|Wu-Hausman F<br>test, p-value|0.70|0.13|0.11|0.18|0.83|0.28|

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: 1971 Infrastructure physical stock, 1971 share of agriculture in GDP, 1971 population density.

28
<!-- Start of picture text --> Figure 13. Country correlations for CE and<br>level of government stability<br>Low Governance Stability High Governance Stability<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4 Low Governance Stability = 0.45***<br>High Governance Stability = 0.39***<br>-0.6<br>-0.8<br>AzerbaijanYemen Côte d'Ivoire Ukraine<br>Oman Congo, Rep.NicaraguaEcuador UruguayGuyanaLiberia Argentina<br>Brazil Namibia NigerBolivia KenyaZimbabwe AngolaQatar<br>Ghana Saudi Arabia MozambiqueThe Gambia Colombia Tanzania LibyaGabon Iceland EthiopiaMongoliaSudan SenegalMali Burkina Faso<br>AlgeriaUzbekistan MyanmarSouth Africa Paraguay<br>Iran, Rep. of Madagascar<br>UgandaCameroon Indonesia Honduras Suriname Togo<br>Russian Federation Papua New Guinea<br>Bahrain Congo, Dem. Rep.<br>NigeriaPeru Iraq<br>Sierra Leone Kazakhstan Guinea-Bissau Costa Rica Botswana Armenia<br>Guinea<br>Chile<br>Zambia<br>Kuwait<br>NorwayMalawi New Zealand<br>Australia Guatemala<br>Brunei Darussalam<br>United Arab Emirates<br>Canada<br>Correlation between cyclical components of real government spending and real GDP 1980-2020<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high (low) government stability, defined as government stability above (below) the sample median. The difference between the two averages is not significant. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively. Sources: Authors’ calculations based on International Country Risk Guide and WEO (IMF) data.
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34
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