rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Results and Discussion**

A simple comparison of averages and distributions shows that learning outcomes in mathematics for grade five students have improved in Uzbekistan despite COVID-19-induced school closures. This improvement is statistically significant and substantial and falls at the high end (around the 85th percentile) of the distribution of effect sizes from randomized control trials of education interventions with standardized achievement outcomes (Kraft 2020). Similarly, a comparison of learning outcomes of students who participated in the assessment in 2019 and in 2021 shows that students’ learning outcomes have improved over time, albeit to a lower extent than the expected growth of 0.80 SD over two years based on international literature. This shows that at a system level, student learning in Uzbekistan did not decline and grade five students in 2021 performed better than grade five students in 2019. However, over the course of two years, students learned less than what they would be expected to learn based on international literature. Uzbekistan implemented nationally representative assessments for the first time in 2019 and 2021 and therefore we lack country-specific data on learning trends to compare the learning growth from 2019 to 2021 to the pre-COVID trend within Uzbekistan.

The distributions of student scores for the mathematics assessment for 2019 and 2021 (grade five and grade seven) are laid out in Figure 1. The student cohort in grade five in 2021 performs significantly better at 0.29 SD than the student cohort in grade five in 2019. Similarly, students in grade seven in 2021 perform significantly better than the average of students in grade five in 2021 and grade five in 2019 (0.43 and 0.72 SD). This difference of 0.72 standard deviations is maintained when limiting the sample to the 3,411 students appearing for the mathematics assessment in 2019 and 2021, thereby limiting any possible bias due to differences in cohort composition (Figure 2).

It is important to understand the possible reasons for this improvement despite COVID-induced school closures and ensure that these improvements can be sustained over time. The results stand in contrast to evidence from other lower- and middle-income countries such as Brazil (Lichand et al. 2022), South Africa (Ardington et al. 2021) and Türkiye (Coskun and Kara 2022), despite the fact that Uzbekistan has a much lower national income per capita than most other countries for which we have robust data. Only Kenya and Ghana have slightly lower income levels than Uzbekistan, and both of those countries had significant learning losses (Whizz Education 2021; Wolf et al. 2022). Uzbekistan’s results, rather, are closer to the results observed in Denmark and France, much higher-income countries, with much shorter durations (8 weeks) of school closures (Birkelund and Karlson 2022; Thorn and Vincent-Lancrin 2021). Additionally, the results presented here are for mathematics, and it is not certain whether similar learning gains will be observed for other subjects.

5
**Figure 2:** The Financial Globalization Index (FGI)

<!-- Start of picture text --> 2 (a) 2 (b)<br>1992 1996 2000 2004 2008 2011 2015<br>1992 1996 2000 2004 2008 2011 2015 year<br>year World Developed<br>Sweden Turkey Emerging Frontier<br>Romania Glob Fact Volatility (RHS) Glob Fact Volatility (RHS)<br>Source: Authors’ computations using Datastream.<br>2 .8 2<br>.8<br>1.5 .6 1.5<br>.6<br>FGI .4 1 FGI .4 1<br>.2 .5<br>.2 .5 Glob Fact Volatility (RHS) Glob Fact Volatility (RHS)<br>0 0<br>0 0<br><!-- End of picture text -->

# **3.** **<u>Financial globalization trends</u>**

To get more precise estimates of the financial globalization trends, we regress the FGIs on a time trend, controlling for country fixed effects. Our results are presented in table 1, which indicates that in the 1992-2016 period, financial globalization increased at an average rate of about 0.6 percentage points a year, see column 1. When we distinguish between developed, emerging and frontier markets, our estimates of the trend (column 4) are, respectively, 0.5, 0.9, and 0.4 percentage points per year. Notice that, had we not adjusted for heteroscedasticity, we would have substantially overestimated the trends, using one (column 2) or ten GFs (column 3).

**Table 1:** Financial globalization trends

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||FGI|UFGI|UFGI10|FGI|UFGI|UFGI10|
|Trend|0.006***|0.013***|0.011***||||
||(0.001)|(0.001)|(0.001)||||
|Trend×D||||0.005***|0.016***|0.013***|
|||||(0.001)|(0.001)|(0.001)|
|Trend×E||||0.009***|0.015***|0.013***|
|||||(0.002)|(0.001)|(0.002)|
|Trend×F||||0.004***|0.006***|0.006***|
|||||(0.001)|(0.001)|(0.001)|
|Constant|0.211***|0.091***|0.296***|0.213***|0.107***|0.308***|
||(0.012)|(0.013)|(0.012)|(0.011)|(0.010)|(0.011)|
|Obs.|1,760|1,760|1,760|1,760|1,760|1,760|
|R-squared|0.097|0.325|0.270|0.117|0.371|0.297|
|Countries|84|84|84|84|84|84|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|
|Clustered stan|dard errors a|t countrylev|el inparenth|eses. ***p<0|.01,**p<0.|05,*p<0.1.|

5
# **B. Latin America and the Caribbean (LAC)**

<!-- Start of picture text --> Back to procyclicality Still procyclical<br>ECUNIC<br>BOL<br>BRA VEN<br>URY<br>HTI PAN ARG<br>COL<br>JAM<br>MEX<br>HND<br>SLV PER GTM<br>DOM<br>TTO<br>CRI<br>PRY<br>CHL<br>Established countercyclical Recent countercyclical<br>-1 -0.50 0 0.50 1<br>Corr(G, GDP) 00-08<br>C. Middle East and North Africa (MENA)<br>Back to procyclicality Still procyclical<br>LBY<br>IRN<br>YEM<br>OMN<br>ISR<br>JOR<br>DZA<br>MAR<br>SAU<br>LBN<br>TUN<br>EGY<br>KWT<br>ARE<br>Established countercyclical Recent countercyclical<br>-1 -0.50 0 0.50 1<br>Corr(G, GDP) 00-08<br>1<br>0.50<br>0<br>Corr(G, GDP) 09-16<br>-0.50<br>-1<br>1<br>0.50<br>0<br>Corr(G, GDP) 09-16<br>-0.50<br>-1<br><!-- End of picture text --> 5
- 18 -

## Flgure _5_

<!-- Start of picture text --> GOVERNMENT DEFICIT AS RATIO OF GDP<br>CCONmItnnt beClC, tnclucting g-ente)<br>11_<br>10<br>9<br>a 4<br>3<br>2<br>I<br>0<br>|GIone rrnya W lwI Tanzania utndc<br>= 191-at5 I gs go<br><!-- End of picture text -->

Source: IMF and World Bank data, and authors' estimates.

# V.2.3 Fiscal Deficit

Fiscal deficits for the five countries are presented in Figure 5, and illustrate that all five countries succeeded in reducing the size of the deficit during the period of reform. In the case of Tanzania, substantial adjustment took place during the reform period, and its overall government budget deficit (after grants) as a ratio of GDP declined from an average of 10.1 percent in the prereform period to 5.6 percent during the reform period.
<!-- Start of picture text --> The Financial<br>The Minister Business Council<br>The Danish<br>Securities Council<br>Minist-y of<br>Economics and The Danish Pension<br>Business Affairs Market Council<br>: Executive i<br>» Management :<br>Regulatory Policy Regulatory Techniques and Supervision Administration<br>InternationalDivision Banking Division InsuranceLife and PetetenDivision ServicesComputerDivision.<br>Legal Division Securities Division DivisioncoOnomics,for Info:Finance,= d<br>Erenrel TseStatistics peisiem and P mersonnelcanal iDivision an<br>Reporting Division<br>Credit and Market<br>Risk Division<br>Investment<br>Companies and .<br>UCITS* Division Mortgage Credit<br>Division<br>Insurance General<br>Mediators and Insurance and<br>Consumers Division Reinsurance<br>Division<br>*Undertakings for Collective Investmentin Transferable Securities<br><!-- End of picture text -->
# **Impact of Property Rights Reform to Support China’s Rural-Urban Integration:**

# **Household-Level Evidence from the Chengdu National Experiment**<sup>**¶**</sup>

Klaus Deininger<sup>†</sup> , Songqing Jin<sup>‡</sup> , Shouying Liu<sup>♯</sup> , Fang Xia<sup>♦</sup> †World Bank, Washington DC ‡Michigan State University, East Lansing MI ♯Development Research Center of the State Council, Beijing ♦Central University of Finance and Economics, Beijing

**JEL Codes O1, O43, R14, R3**

**Keywords: Urban development, land use change, China, financial development**

> ¶ Email: <u>kdeininger@worldbank.org; jins@anr.msu.edu; liusy@drc.gov.cn; xia.fang.fx@gmail.com. We thank NBS, in particular Pingping Wang,</u> Wei Wu, Yilin Feng, and Jianliang Di, as well as the Chengdu Statistical office, especially Taixiang Zhao, Xiaoying Gu, Yuan Lin, Yufang Chen, Qiuyan Chen, and Shiming Ren for access to data and support, Ting Shao for communication with NBS and the Chengdu Statistical office, and Yihao Li for research assistance. Funding support from the Knowledge for Change Program is gratefully acknowledged.
# **3 Data and Calibration**

The strength and duration of a lockdown are critical aspects of our quantitative analysis. Our _reference_ lockdown policy attempts to mimic what had been observed in the first weeks of the pandemic. It is henceforth defined by three parameters: its starting time, length, and severity. To calibrate these parameters alongside transmission rate parameters, we proceed in two steps. In the first step, we calibrate the transmission parameters _πI_ 1,... _πI_ 4 to match the relative importance of the different transmission modes and the overall predicted infection rate in an unmitigated spread scenario. In the second step, we calibrate the effect of lockdown severity on community transmission _ξ_ , alongside the strength of the reference lockdown _μ_ ̄, to jointly match the decline in GDP and the reproduction number _R_ 0 as estimated by Flaxman et al. (2020) for European countries early in the pandemic.

## **3.1 Infection and Mortality Parameter Calibration**

The within-household transmission parameter, _πI_ 4, is taken from a meta-analysis of household transmission estimates from different settings. Lei et al. (2020) estimate the secondary infection rate in the household to be 0.27.<sup>13</sup> To discipline the three other transmission parameters, we jointly match three moments. The first moment is the proportion of the population that would get infected in each country in the absence of any mitigating policy. We use projections reported by Walker et al. (2020) that use country- and age-specific contact patterns to simulate health impacts of COVID-19 in 202 countries. They develop an SIR model incorporating the age distribution of each country. Employing a basic reproduction number ( _R_ 0) of 3.0, they project that about 90 percent

> 13Household living conditions in poor and rich countries differ, and thus it would have been desirable to calibrate country-group-specific within-household transmission rates. Unfortunately, we are not aware of any systematic studies on country heterogeneity in within-household transmission. The value in Lei et al. (2020) is based on various settings, including several estimates from China. A recent paper based on data for Norway (Telle et al., 2021) finds a secondary attack rate (SAR) of 0.24 when the index case is a parent or a child under 6, and a SAR of 0.14 when the index case is an older child. It is thus reassuring that the rates for Norway are similar to those for China, two countries at different levels of development (and with different levels of housing area per resident). There is a feature of our calibration that accommodates the notion that slums or other high-density living arrangements can lead to higher transmission rates. Namely, we calibrate substantially higher community transmission rates in lower- and middle-income countries compared to wealthier ones. This difference can be thought of as capturing the prevalence of high-density living such as slum areas.

21
WPS3938

# **Creating a More Efficient Financial System: Challenges for Bangladesh**

## **Thorsten Beck and Md. Habibur Rahman**

**Abstract:** While Bangladesh has embarked on a path to reform its financial system, most prominently by privatizing its government-owned banks, the Nationalized Commercial Banks (NCBs), a sustainable long-term expansion of the financial system requires a more substantial change in the role of government.  Using recent research and international comparisons, this paper argues that the government should move from its role as an operator and arbiter in the financial system to a facilitator role.  This implies not only divestment from government-owned banks, but also de-politicization of the licensing process and a market-based bank failure resolution framework that focuses on intermediation and not on the rescue of individual institutions. Most importantly, the government should move away from the implicit guarantee for depositors and owners to applying the existing limited explicit deposit insurance for depositors, while simultaneously relying more on market participants to monitor and discipline banks instead of micro-managing financial institutions. This redefinition of government’s role should not be limited to the banking system, but applies to other segments of the financial system, such as capital markets and the micro-finance sector, and should be seen as an essential element in the governance reform agenda and in the movement from a relationship-based economy to a market and arms-length economy.

### World Bank Policy Research Working Paper 3938, June 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._

* Beck: World Bank, Rahman: Bangladesh Bank. This paper is a background paper for: “Bangladesh: Strategy for Growth and Employment”.  Comments and suggestions from Juan Costain, Aurora Ferrari, Sandeep Mahajan and participants of seminars in the World Bank in Dhaka and Washington D.C. are gratefully acknowledged. We are grateful to Edward Al-Hussainy for outstanding research assistance.
28

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# **4. Measured innovation outcomes in the short and long questionnaire**

## 4.1 **Innovation rates in the two surveys**

Results from the two stages of data collection were expected to show some variation given the differences in the two survey instruments and the subjectivity of the questions asked. The extent of these differences, however, is surprisingly high. Overall, in the full sample more firms reported innovation in the short questionnaire as compared to the long questionnaire in all types of innovation but marketing (Figure 1).<sup>10</sup> The magnitude of these differences varies considerably, ranging from 1 percentage point for R&D to 14 percentage points for process innovation. Moreover, high differences in reported innovation rates are found in all three components of process innovation with the highest difference being in auxiliary activities and logistics (14 percentage points difference) and the smallest in methods (9 percentage points).

Despite the lower innovation rates in the long questionnaire, the proportion of firms that reported innovations are still higher than expected: 19 percent of firms in the full sample engaged in R&D activities, 39 percent of firms reported product innovations and 45 percent of firms reported innovations in processes. Marketing innovations showed an opposite pattern with fewer firms reporting innovation in the short questionnaire (47 percent) compared to the long questionnaire (68 percent). The fact that in the long questionnaire marketing methods were disentangled into several components, each of them with a specific yes/no question, can explain this opposite pattern. In this case the problems with subjective data measurement apply for each of the components in marketing methods and get, therefore, magnified in the complied measure.

The existence of considerable variation in self‐reported innovation is also confirmed at the country level. As Figure 2 shows, differences in reported innovation are sizable across types of innovations and countries.<sup>11</sup> However, the magnitude and direction of these differences vary widely and no country shows consistently lower or higher differences across types of innovation. Despite this heterogeneity, some patterns can be identified. First, with the only exception of Bangladesh, India, South Sudan, and Zambia, innovation rates in product and process are higher when measured by the short questionnaire. Moreover, differences in innovation rates for these two types of innovation are high in most of the countries. Second, 10 Innovations rates are computed by using sampling weights. Overall averages are computed as simple average of weighted country‐level averages.

> 11 A simple test of the equality of the means shows that we can only accept equality of the mean between the short and long survey measure for product, process and marketing innovation in DRC, marketing innovation in Kenya and R&D for Ghana, Nepal and South Sudan.

12
**Figure 3: Death probability by rural/urban location of family in 1994. From siblings' mortality data. Demographic and Health Survey, 2000, Rwanda.**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>1 to 5 6 to 10 11 to 15 16 to 20 21 to 25 26 to 30 31 to 35 36 to 40 41 to 45 46 to 50 Above 50<br>Age in 1994<br>Sister living in urban area in 1994 Sister living in rural area in 1994<br>Death probability<br><!-- End of picture text --> 23
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**Table 5: Utilization and satisfaction: public health system**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Utilization|Satisfied<br>(0-1)|Satisfied<br>(0-1)|Satisfacti<br>on level<br>(1-5)|Satisfacti<br>on level<br>(1-5)|
|||||Ordered|Ordered|
||Probit|Probit|Probit|probit|probit|
|**Household-level variables**||||||
|_Consumption group_||||||
|Lower|-0.456***|0.146***|0.0908*|0.0947**|0.0413|
||(0.0384)|(0.0565)|(0.0545)|(0.0452)|(0.0452)|
|Middle|-0.137***|0.164***|0.112**|0.163***|0.111***|
||(0.0380)|(0.0420)|(0.0435)|(0.0357)|(0.0358)|
|Upper||Refe|rence catego|ry||
|_Locality_||||||
|Rural|-0.0628*|0.0650|0.0585|0.0163|0.00467|
||(0.0351)|(0.0409)|(0.0415)|(0.0348)|(0.0343)|
|Metropolitan|-0.146***|-0.0422|-0.000152|-0.105**|-0.0591|
||(0.0559)|(0.0622)|(0.0631)|(0.0529)|(0.0527)|
|Urban||Refe|rence catego|ry||
|Number of children (0-14)|0.181***|||||
||(0.0230)|||||
|Number of women aged 15-44|0.0769***|||||
||(0.0235)|||||
|Number of men aged 15-44|0.137***|||||
||(0.0246)|||||
|Number of women aged 45-64|0.0102|||||
||(0.0367)|||||
|Number of men aged 45-64|0.212***|||||
||(0.0380)|||||
|Number of women aged 65+|0.130**|||||
||(0.0506)|||||
|Number of men aged 65+|0.272***|||||
||(0.0483)|||||
|**Individual-level variables**||||||
|_Health status of respondent_||||||
|Very good|-0.232***|0.423***|0.363***|0.360***|0.284***|
||(0.0535)|(0.0681)|(0.0721)|(0.0602)|(0.0616)|
|Good|-0.224***|0.216***|0.142***|0.181***|0.111***|
||(0.0373)|(0.0472)|(0.0482)|(0.0398)|(0.0400)|
|Medium||Refe|rence catego|ry||
|Bad|0.195***|-0.180***|-0.141**|-0.180***|-0.143***|
||(0.0545)|(0.0593)|(0.0612)|(0.0508)|(0.0506)|
|Very bad|0.258**|-0.235**|-0.141|-0.200**|-0.115|
||(0.106)|(0.0978)|(0.0951)|(0.0910)|(0.0917)|
|_Education of respondent_||||||
|<br>Lower secondary or below||Refe|rence catego|ry||
|<br>Upper secondary|-0.0502|-0.0725<br>|-0.0649<br>|-0.101**<br>|-0.0906**|
||(0.0413)|(0.0502)|(0.0512)|(0.0422)|(0.0418)|
|Post secondary or higher|-0.0551|-0.0906*|-0.0406|-0.109**|-0.0659|
||(0.0431)|(0.0513)|(0.0532)|(0.0425)|(0.0425)|

18