rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 7956

# **Abstract**

School enrollment has universally increased over the past 25 years in low-income countries. However, enrolling in school does not guarantee that children learn. A large share of children in low-income countries learn little, and they complete their primary education lacking even basic reading, writing, and arithmetic skills—the so-called “learning crisis.” This paper uses data from nationally representative surveys from seven Sub-Saharan African countries, representing close to 40 percent of the region’s total population, to investigate possible answers to this policy failure by quantifying teacher effort, knowledge, and skills. Averaging across countries, the paper finds that students receive two hours and fifty minutes of teaching per day—or just over half the scheduled time. In addition, large shares of teachers do not master the curricula of the students they are teaching; basic pedagogical knowledge is low; and the use of good teaching practices is rare. Exploiting within-student, within-teacher variation, the analysis finds significant and large positive effects of teacher content and pedagogical knowledge on student achievement. These findings point to an urgent need for improvements in education service delivery in Sub-Saharan Africa. They also provide a lens through which the growing experimental and quasi-experimental literature on education in low-income countries can be interpreted and understood, and point to important gaps in knowledge, with implications for future research and policy design.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2015: Realizing the Promise of Education for Development_ —is a product of the Education Global Practice Group, the Development Research Group, and the Africa 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 authors may be contacted at tessa.bold@iies.su.se.

_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
Results for most other variables in equation 1.2 are very similar to those in equation 1.1. The most notable difference is that the coefficient for non-DAC bilaterals increases in magnitude from -15 to -23. Controlling for their very high levels of support for aid, Korea and Turkey stand out even more strongly as negative outliers on use of country systems. It is not clear how to interpret the public opinion results for these two countries, however. Korea and Turkey were historically aid recipients, and their aid outflows are still small. The public opinion index for them is measured using only the GI 2002 survey, in which 87% of respondents in each country thought that “the wealthier nations should give more financial help to the poorer nations.” Many respondents in both countries might not have considered their own country to be among the “wealthier nations,” and some (particularly in Turkey) may even have considered their own country to be among the “poorer nations” on the receiving end. In equation 1.3 we therefore drop the 52 observations in which Korea or Turkey are the donors. Results for the public opinion variable (and for the other regressors) are unaffected by this change in sample, however.

The remainder of this section addresses three potential problems with the basic results reported in equations 1.1-1.3. First, those estimates are potentially affected by sample selection bias. Second, there may be omitted variables correlated with use of country systems. Third, coefficients for some key regressors may incorporate endogeneity bias.

There are two sources of potential selection bias in the data. First, aid-recipient governments self-select into (or out of) the PDMS. Of the 151 eligible aid recipients on the official DAC list, only 80 participated in one or more rounds of the survey. Second, not all donors choose to provide aid to all recipients in all years (or, in some cases, recipients may choose not to accept aid). In fact, for the vast majority of all possible donor-recipient pairs, no aid flows are recorded, as shown in Appendix Table A3. For 2010, the year with the highest 18
|% of poor households with NFEs|16.0|23.0|19.5|19.9|
|---|---|---|---|---|
|% of nonpoor households with NFEs|27.1|26.6|**32.3**|**22.0**|

Author’s calculations using ESS 2018/2019 (period 1) and 2021/2022 (period 2). Notes: Female-only households are households with no male adult present. Poor households are defined as those in the bottom 40% of the adult equivalent expenditure distribution. Significant differences at p _<_ 0.05 are shown in bold.

**Table 2: Probability that a household owns a nonfarm enterprise**

|VARIABLES|(1)<br>Period 1|(2)<br>Period 2|(3)<br>Period 1|(4)<br>Period 2|
|---|---|---|---|---|
|Female only household = 1|0.542***|0.392*|||
||(0.169)|(0.236)|||
|Log of household consumption|0.062***|0.047***|0.063***|0.047***|
||(0.012)|(0.015)|(0.012)|(0.016)|
|Female only household * log of household consumption|-0.053***|-0.041*|||
||(0.017)|(0.023)|||
|Household size|0.036***|0.028***|0.034***|0.029***|
||(0.005)|(0.005)|(0.005)|(0.004)|
|Rural|-0.181***|-0.204***|-0.181***|-0.205***|
||(0.021)|(0.027)|(0.021)|(0.027)|
|Female headed household = 1|||0.351***|0.189|
||||(0.133)|(0.187)|
|Female headed household * log of household|||||
|consumption|||-0.035**|-0.021|
||||(0.013)|(0.019)|
|Observations|6,770|4,959|6,770|4,959|
|R-squared|0.075|0.069|0.074|0.069|

Source: Author’s calculations using ESS 2018/2019 and 2021/22. Notes: Coefficients are significant at *** p<0.01, **p<0.05, * p<0.1.

Coefficients from a linear probability model using OLS regression are presented. OLS regression includes the number of children disaggregated by age groups, household size, marital status of household head, age of household head, whether the household uses electricity for cooking or lighting, dummy variables for regions, and the religion of the household head, but are not shown. Standard errors in parentheses are clustered at the enumeration area. Female-only households are households with no male adult present. Household consumption is the spatially-adjusted adult equivalent scale household consumption.

Of the female entrepreneurs, 40% are divorced, widowed, or unmarried, and approximately 40% live in female-headed households (in Appendix 4). There is some evidence that the composition of enterpriseowning households changed between the survey periods. In period 1, 32% of female entrepreneurs live in households without male adults, but this percentage drops to 9.5% in period 2. The absence of male adults in the households is likely to be disadvantageous for women wanting to engage in more profitable activities. Studies have shown that spousal support or male mentors could play an important role in helping female entrepreneurs enter male-dominated and more profitable sectors (Alibhai et al., 2017, World Bank 2022).

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<!-- Start of picture text --> Gauteng - Distribution of travel time to work in 2009<br>40<br>35<br>30<br>yy3 25<br>ZG<br>a<br>5<br>% 20<br>£<br>a<br>2<br>2 15<br>10<br>5<br>0<br>0-15 16-30 31-60 61-90 >90<br>source: Stats SA 2009 One way travel time to work in minutes<br><!-- End of picture text -->
**Figure 4a Key obstacles to bank involvement with SMEs, by type of country**

<!-- Start of picture text --> 100% Developed Developing<br>80%<br>60%<br>45%<br>39% (5)<br>(28)<br>40%<br>17% 15% 18%<br>20% 9% 9% (12) 8% 9% 8% 9% (11) (2)<br>(1) (1) (6) (1) (6) (1) 6% 6%<br>(4) (4)<br>0%<br>Macroeconomic Regulations Legal and Bank specific Nature of the Competition in Lack of adequate<br>(economy-wide) contractual factors lending the SME demand<br>factors environment technology to segment<br>SMEs<br>Figure 4b<br>Key obstacles to bank involvement with SMEs, by type of bank<br>100%<br>Government Private (Domestic) Foreign<br>80%<br>60%<br>39%<br>37% 38%<br>(11)<br>40% (14) (6)<br>25% 25%<br>(4) (7)<br>18%<br>20% 13% 13% (7)<br>11%<br>7% (5) 6% (5) 6% (3) 8% 6% 7% 5% 6% 6% 8%<br>(2) (1) (1) (3) (1) (2) (2) (1) (1) 4% (3)<br>(1)<br>0%<br>Macroeconomic Regulations Legal and Bank specific Nature of the Competition in Lack of adequate<br>(economy-wide) contractual factors lending the SME demand<br>factors environment technology to segment<br>SMEs<br>Percentage of banks<br>Percentage of banks<br><!-- End of picture text --> 27
the school, via the headmaster. Spending decisions were made at the Annual General Meeting, before the start of the school year. The share of this funding in overall school funding was considerable: for 76% of schools it was the only public funding for non-salary inputs, while its average share in total school resources was 86%. The scheme also attracted much publicity, increasing its transparency; combined with the simplicity of the allocation rule, this ensured that the grants reached their intended recipients. Disbursement was fast and reliable and 95 percent of all schools had received the stipulated amounts by the time of the survey and the remainder within 1 month of survey completion (Das et al. 2003).<sup>14</sup> Therefore, we expect that in the year of the survey the fixed cash grants would be anticipated by households making their educational investment decisions for the year, contrary to discretionary sources, which had become highly unpredictable and therefore unanticipated. Furthermore, because the grants were fixed in size, there was considerable variation across schools in per-student terms due to underlying differences in enrollment. We use the variation in per-student amounts to examine the crowding-out of household expenditures, a strategy discussed further below.

# **3.2 Sampling and Data**

We collected data in 2002 from 172<sup>15</sup> schools in 4 provinces of Zambia (covering 58 percent of the population), where the schools were sampled to ensure that every enrolled child had an equal probability of inclusion. The results are therefore externally valid within the 4 provinces of the study. The school surveys provide basic information on school materials and funding as well as test scores for mathematics and English for a sample of 20 students in grade 5 in every school, who were tested in 2001 as part of an independent study and were then retested in 2002 to form a panel.

To supplement these data, we also collected information for 540 households matched to a sub-sample of 34 schools identified as "remote" using GIS mapping tools (defined as schools where the closest neighboring school was at least 5 kilometers away). From these schools, the closest village was chosen and 15 households were randomly chosen from households with at least one child of school-going age. The restriction of the household survey sample to 34 remote

> 14 This contrasts with the early experience in Uganda (Reinnika and Svensson 2004).

> 15 The initial sample contained 182 schools, although 2 yielded only incomplete information, 5 were private schools not covered in this paper and 3 could not be matched to the test scores data from the Examination Council of Zambia.

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_Figure 9: Economic Internal Rate of Return (%)_ <!-- Start of picture text --> 25 23.3<br>20.5<br>19.6<br>18.9<br>20<br>16.5<br>15<br>11.9<br>10.3<br>10<br>5<br>0<br>Public Private Public Private Public Private<br>Achievers of higher HS Achiever of lower HS<br>grades grades<br>DTS RP College<br>Percent<br><!-- End of picture text --> Source: _DTS Tracking Survey, 2015, World Bank. World Bank calculations._ 25
There are many explanations for this low-intermediation trap in Brazil. The adverse macroeconomic environment is an important cause, but not the only. This note analyzes the importance of legal and regulatory impediments in explaining the level and the efficiency of financial intermediation in Brazil. Using cross-country experiences, it quantifies the negative effects of impediments in the Brazilian legal system on the development and efficiency of the financial sector and therefore on economic growth. Section 2 discusses the institutional framework (creditor rights, their enforcement, accounting standards and credit information systems) in Brazil. Section 3 uses intemational evidence to stress the importance of these institutional features for financial intermediation. Section 4 shows the importance of the institutional framework for the size of net interest margins. Section 5 discusses policy implications for Brazil and.areas for reforms.

# 2. Creditor Rights, Contract Enforcement and **Information Sharing in** Brazil

The ability of creditors to enforce claims is key to the development of an efficient financial system. On an international comparison, Brazil's creditor rights are neither specifically weak, nor specifically strong (Table 2). In reorganization and liquidation, Brazil compares favorably on some dimensions; there is no automatic stay on secured assets upon filing of a reorganization petition, thereby not preventing secured creditors from taking possession of collateral, and there are restrictions when debtors file for reorganization: unsecured creditors representing a minimum share of claims can oppose the petition for reorganization on certain grounds, and secured creditors are not bound by the reorganization plan. On the other hand, the claims of secured creditors are not ranked first in the case of liquidation, but rank after claims of workers and tax authorities. Finally, management stays in charge of the operation of their business during reorganization, thus decreasing the power of creditors.

Table 2: Creditor rights **and** contract **enforcement in Brazil relative to other countries**

||Creditor rights|Contract enforceability|
|---|---|---|
|Brazil|2|2.02|
|Sample mean|2.32|2.50|
|Latin America|1.38|1.92|
|Of which: Argentina|1|2.07|
|Chile|2|2.42|
|Mexico|0|1.83|
|Upper middle income<br>countries|2|2.14|
|OECD countries|1.82|2.99|

Credito **r** ights: index of secured creditor rights, with values between 0 (worst) and 4 (best). Source: La Porta et al. (1997). Contract enforceability: indicator of the degree to which contracts are honored, with values between 1 (worst) and 4 (best)-. Source: Business International Corporation.

Regardless of the presence of creditors rights, these rights need to be enforced, and in this dimension, Brazil's judicial system scores considerably below countries in the upper middle income group and OECD countries, as evidenced by a cross-country indicator of contract 3
26 between the poor and the non poor grows narrower after the age of 25, especially regarding labor force participation rates which converge to 50 percent for both groups.

_Low participation and employment rates among youth are only partially due to schooling._ Table 16 displays the reasons for which youth are out of the labor force. Although nearly 50 percent of the nonparticipants aged 15 to 19 are in school, 18.1 percent of them report being out of the labor force because there is no work and 20 percent because of family or household obligations. As far as the 20 to 24 age group is concerned, non-participation is mainly due to family reasons (41.2 percent), as the proportion of students among them only stands at 27.2 percent.

**Table 16. Youth Out of the Labor Force – Motives (in %)**

|||_15-19_<br>||<br>|_20-24_<br>|<br>|<br>|_25-2_<br>|_9_<br>|
|---|---|---|---|---|---|---|---|---|---|
||_All_|_Male_|_Female_|<br>_All_|_Male_|_Female_|<br>_All_|_Male_|_Female_|
|_No work_|18.1|19.9|16.6|25.9|37.7|20.9|20.9|44.9|16|
|_Slack season_|0.6|0.9|0.3|0.7|1.9|0.2|2|7.4|0.9|
|_Student_|49.9|60.4|41.5|27.2|45.4|19.4|8.6|27.8|4.7|
|_Family/household_<br>_obligations_|20|5.8|31.4|41.2|7.4|55.7|64|9.5|75.1|
|_Too young/old_|9.9|11.6|8.5|2.8|4.4|2.1|1.8|2.3|1.7|
|_Disabled_|1.3|1.3|1.3|1.3|2.1|1|2.1|7.2|1|
|_Other_|0.2|0.1|0.3|0.8|0.9|0.8|0.6|0.9|0.6|
|_Total_|100|100|100|100|100|100|100|100|100|

Source: EPCV 2004.Total might not always exactly amount to 100 due to rounding.

_Youth’s participation in the labor market decreases with education._ As illustrated by Figure 12 youth with no education exhibit a 47.9 percent rate of labor force participation and a 31.6 percent employment rate, whereas these figures stand respectively at 31.4 percent and 22.5 percent for young people with primary school education. This is partly due to the fact that 46.7 percent of the 15 to 24 age group is in school. However, labor force participation among unschooled people of the same age stands at 48 percent only, in other words 52 percent of the youth who are not in school are not in the labor force either (results not shown here). Finally, for those who do participate in the labor market, unemployment rates remain high, regardless of their level of education. They hover between 30 and 35 percent for all groups and peak at 49.7 percent for people with secondary schooling.

_Once employed, young people are either self-employed or work as unpaid family labor or apprentices._ Figure 13 shows that among those aged 15 to 24, 33 percent are self-employed and 31 percent are unpaid family labor or apprentices. Compared to their elders, young workers are more likely to serve as unpaid labor or apprentices (31 percent compared to only 4 percent for the 25-to-64 employed age group) and fewer of them are self-employed (33 percent compared to 56 percent for the
The main set of policies and institutions that might have an impact on the survival and growth of recently created firms is the **government interference** in the day-today operations of business. That includes a wide range of factors, going from business regulations to corruption. They can be grouped for operational purposes in four categories: legal system, market regulations, size of government and corruption level. The **tax system** , both the tax burden and tax regulation, is also consistently reported as hampering firms’ survival and growth.

# **_The impact of the investment climate on labor market outcomes_**

It has been estimated using data for a large group of ECA countries from the early 1990s and multivariate regression analysis that the **most important components** of business environment affecting employment outcomes **are the access to finance, market regulations and the start-up costs** . The tax burden is especially important in the new EU members. The legal system and corruption appear to be important in those countries less advanced in their transition towards a market economy.

The analysis presented in this paper suggests that reforms aimed at making it easier and cheaper for new firms to access the required finance to start and expand a business might help job creation, through firm entry, in the new private sector of the ECA economies. Lowering the administrative burdens to start a business and deregulating the credit and labor markets have also a positive impact on employment.

The paper is organized as follows. The next section introduces briefly the institutions shaping the investment climate of a country. A preliminary analysis, using bivariate correlations and graphs, of the impact of the investment climate on the labor market is carried out in section II. The graphical analysis is followed by a multivariate analysis that allows a more precise estimation of the effect of each institution after controlling for a number of variables. Lastly the business environment within the ECA regions is analyzed. The objective of this analysis is to facilitate the identification of idiosyncrasies of each region, in order to improve the investment climate and hence to increase firm density and job creation.

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could argue for a set of hypotheses that CP&A problems should be reduced by income per capita and that access to better talent and systems might also make it easier to implement counter-cyclical financing and accurate compositional outcomes. The unit of measure is the natural logarithm of gross national income per capita, denominated in purchasing power parity adjusted US dollars.

||**TABLE6**—**SUMMAR**|**Y OFHYPOTHESES**||
|---|---|---|---|
||**Equation 9**|**Equation 10**|**Equation 11**|
||<br>**Aggregate Deviation, ****_r_**|<br>**Aggregate Deviation, ****_e_**|<br>**Compositional Accuracy, ****_fb_**|
|_r_|**1**|**..**|**..**|
|_e_|**..**|**1**|−φ_1_∙|_e_||
|_fb_|**..**|**..**|**1**|
|**Constants**|+ ρ_0_|+ε_0_|+ φ_0_|
|_Rp_|+ ρ_1_∙_Rp_|**..**|**..**|
|_Rv_|+ ρ_2_∙_Rv_|**..**|**..**|
|_PCT_INT_|− ρ_3_∙_PCT_INT_|+ ε_1_∙_PCT_INT_|− φ_2_∙_PCT_INT_|
|_PCT_INT_|+ ρ_4_∙_Rp _∙ Z(_PCT_INT_)|+ε_2_∙_rpos _∙_Z(PCT_INT)_|**..**|
|**Common Pool Variables, ****_Y_**||||
|_H01 FCTN_ETHN_|+ ρ_5_∙_FCTN_ETHN_|+ ε_3_∙_FCTN_ETHN_|− φ_3_∙_FCTN_ETHN_|
|_H01 FCTN_LANG_|+ ρ_6_∙_FCTN_LANG_|+ ε_4_∙_FCTN_LANG_|− φ_4_∙_FCTN_LANG_|
|_H01 FCTN_RELIG_|+ ρ_7_∙_FCTN_RELIG_|+ε_5_∙_FCTN_RELIG_|− φ_5_∙_FCTN_RELIG_|
|_H02 DRY 1/_|+ ρ_8_∙_DRY_|+ ε_6_∙_DRY_|− φ_6_∙_DRY_|
|_H02 DRO_|+ ρ_9_∙_DRO_|+ ε_7_∙_DRO_|− φ_7_∙_DRO_|
|_H03 PCT_CSE 1/_|+ ρ_10_∙_%CSE_|+ ε_8_∙_%CSE_|− φ_8_∙_%CSE_|
|**Political Institutions, X**||||
|_H04 POLITY 2/_|− ρ_11_∙_POLITY_|− ε_9∙ POLITY_|+ φ_9_∙_POLITY_|
|_H05 MPP_|− ρ_12_∙_MPP_|− ε_10∙ MPP_|+ φ_10_∙_MPP_|
|_H06 SYSTEM_|− ρ_13_∙_SYSTEM_|− ε_11∙ SYSTEM_|+ φ_11_∙_SYSTEM_|
|_H07 PR_|− ρ_14_∙_PR_|−ε_12∙ PR_|+ φ_12_∙_PR_|
|**Capacity for PFM, X**||||
|_H08 PEFA_|− ρ_15_∙_PEFA_|− ε_13_∙_PEFA_|+ φ_13_∙_PEFA_|
|_H09 ln(POP) 2/_|− ρ_16_∙_POP_|− ε_14_∙_POP_|+ φ_14_∙_POP_|
|_H10 ln(GNIPC)_|− ρ_17_∙_GNIPC_|−ε_15_∙_GNIPC_|+ φ_15_∙_GNIPC_|
|_H11ODAGDP_|− ρ_18_∙_ODAGDP_|− ε_16_∙_ODAGDP_|_+_ φ_16_∙_ODAGDP_|
|**Interactive Variables**||||
|_H03 PCT_CSE 1/_|+ ρ_19_∙_Rp _∙ Z(_PCT_CSE_)|+ ε_17_∙_r_∙_Z(PCT_CSE)_|**..**|
|_H04 POLITY 2/_|+ ρ_20_∙_Rp _∙ Z(_POLITY_)|+ ε_18_∙_r_∙ Z(_POLITY)_|**..**|
|_H05 MPP_|+ ρ_21_∙_Rp _∙ Z(_MPP_)|+ ε_19_∙_r_∙ Z(_MPP)_|**..**|
|_H06 SYSTEM_|+ ρ_22_∙_Rp _∙ Z(_SYSTEM_)|+ ε_20_∙_r_∙ Z(_SYSTEM)_|**..**|
|_H07 PR_|+ ρ_23_∙_Rp _∙ Z(_PR_)|+ ε_21_∙_r_∙ Z(_PR)_|**..**|
|_H08 PEFA_|+ ρ_24_∙_Rp _∙ Z(_PEFA_)|+ε_22_∙_r_∙_Z(PEFA)_|**..**|
|_H09 ln(POP)_|+ ρ_25_∙_Rp _∙ Z(_POP_)|+ ε_23_∙_r_∙_Z(POP)_|**..**|
|_H10 ln(GNIPC)_|+ ρ_26_∙_Rp _∙ Z(_GNIPC_)|+ ε_24_∙_r_∙_Z(GNIPC)_|**..**|
|_H11ODAGDP_|+ ρ_27_∙_Rp _∙ Z(_ODAGDP_)|+ ε_25_∙_r_∙_Z(ODAGDP)_|..|
|_H12 BATTLE_|+ ρ_28_∙_BATTLE_|+ε_26_∙_BATTLE_|− φ_17_∙_BATTLE_|

1. If this variable takes the opposite sign from what is predicted, then it may be more relevant to PFM capacity.

2. If this variable takes the opposite sign from what is predicted, then it may be more relevant as a common pool variable.

**_H11:  External assistance_** .  Many governments are supported financially by external donors for a wide variety of purposes.  The impact could be positive in some ways, increasing the pace of

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