rafmacalaba/datause-encoder-data huggingface dataset:
to capture early grade learning in relation to what students are expected to acquire in the first few years of primary school in Indonesia. The general cognitive items on the test are based on the Raven’s Colored Progressive Matrices.

Two versions of the test were administered: one for younger children ages 6 and 7, which had a total of 52 items and another one for older children ages 8 and 9, which had a total of 64 items. For both tests, the language section tested children’s ability to recognize letters and words, match words to objects, and comprehend short reading passages. The mathematics section tested children’s abilities to add, subtract, and order one to two digit numbers. The items based on Raven’s Progressive Matrices were intended to measure children’s general cognitive skills.

We calibrated the test score of children into a common scale using the 6 year-olds as the reference group. We used the mean and standard deviation of the 6 year-old group to normalize the test scores. The advantage of normalizing using the 6 year-olds as a reference group is the ease of interpretation, as we can understand the magnitude of increase in test scores associated with increase in age. The typical child in the sample scored 0.63 SD on language, 0.54 SD on mathematics and 0.28 SD on cognitive skills (see Table 1).

# <u>Early childhood histories</u>

ECED enrollment histories were collected by asking children’s primary caregivers to retrospectively report the types of ECED service a child had ever been enrolled in (including “no ECED” as a type) and the number of months enrolled in each type of ECED during each academic year since 2008-2009. Based on this information, we generate a variable for ECED sequence, which denotes the sequence of ECED programs attended by each child in the sample. In addition, based on a child’s age at the time of the survey in 2013, we are able to extrapolate the age of entry into ECED. We generate a variable that captures timing, which is the age of the child at the time of entry into each ECED service sequence. We also know the months of enrollment in each type of ECED service, which allows us to create a variable for duration. Duration was categorized into three groups: less than one year, between one and two years, and greater than or equal to two years. Table 1 presents the mean and standard deviation of each of these variables.

For ECED sequence, we find the largest category to be those who enrolled in kindergarten prior to primary school (35.2 percent). Next are children who did not participate in any ECED programs (19.6 percent), followed by those who enrolled in playgroup prior to primary 8
# **Figure 9. Tax revenue composition in small states and other EMDEs**

_Compared with other EMDEs, in small states trade taxes account for a higher share of tax revenues and consumption taxes a lower share. However, there is substantial variation in the composition of tax revenue among small states, with income taxes accounting for a higher share of revenue in EAP, while in other regions and in tourism-dependent small states consumption taxes are more important._

## **A. Composition of tax revenue, 2011-22**

<!-- Start of picture text --> Percent Consumption Trade<br>Income Property<br>100<br>80<br>60<br>40<br>20<br>0<br>Small states EMDEs excl. small<br>states<br><!-- End of picture text -->

## **C. Composition of tax revenue by trade composition, 2011-22**

<!-- Start of picture text --> Percent Consumption Trade<br>Income Property<br>100<br>80<br>60<br>40<br>20<br>0<br>Small states: Small states: Small states:<br>Commodity Commodity Tourism-reliant<br>exporters importers<br><!-- End of picture text -->

**B. Composition of tax revenue by region, 201122**

<!-- Start of picture text --> Percent Consumption Trade<br>Income Property<br>100<br>80<br>60<br>40<br>20<br>0<br>EAP small LAC small Other small<br>states states states<br>D. Composition of tax revenue by population<br>size, 2011-22<br>Percent Consumption Trade<br>Income Property<br>100<br>80<br>60<br>40<br>20<br>0<br>Micro states Other small states<br><!-- End of picture text --> _Sources_ : GFS (database); International Monetary Fund; World Bank.

_Note_ : EAP = East Asia and Pacific; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean.

A.-D. Bars show average consumption, trade, income, and property taxes as share of total tax revenues for period 2011-22.

A. Sample includes 28 small states and 95 other EMDEs.

- B. EAP small states include 11 economies, LAC small states include 8 economies, and other small states include 9 economies.

C. Small states commodity exporters include 9 economies, small states commodity importers include 19 economies, and tourism-reliant small states include 21 economies.

D. Micro states have a population of less than 200,000. Micro states include 12 economies, and other small states include 16 economies.

39
concentration of the export sector. This explanation is also akin to that advanced by Klapper, Laeven and Rajan (2006) for firms’ dwarfism in countries like Italy. Overall, our findings for the effect of financial structures on export concentration may then offer more support to the SBC hypothesis than to the banks’ long-termism hypothesis.

## **6.2 Further robustness tests**

In Table 7, we carry out several robustness checks for our baseline results. In Panel A, we experiment with instrumenting the relative presence of foreign banks with the indicator of “banking sector entry barriers” constructed by Abiad et al. (2008). This indicator is an inverse measure of the extent to which the country imposes entry restrictions in the domestic banking market, especially on foreign banks: it takes values from 0 to 3, with 0 representing the highest barriers and 3 the lowest.<sup>22</sup> We lag this indicator by five years to allow for delays in the impact of the entry regulation on the operations of foreign banks in the country. The first-stage estimates show that bank entry regulation significantly affects the relative presence of foreign banks (as expected, lower entry barriers imply larger presence of foreign banks). The second-stage estimates confirm the baseline results: foreign banks appear to promote entry into, and exit from, the export sector, boosting the turnover of exporters.

In Panel B of Table 7, we experiment by considering alternative indicators of institutional quality of the country in the analysis of the mechanisms of influence. We use credit registry coverage of adults as an alternative measure of the information available to banks. Similar to the results in Table 5a, Panel A, adding this control for the quality of information has no impact on the coefficients on the interaction of external finance dependence with the relative importance of foreign banks.

Further, we interact a measure of government effectiveness with the indicator of information complexity in Panel C, and with the indicator of contractual complexity in Panel D. These estimates are qualitatively similar to the results in Panels B and C of Table 5a and Table 5b.

# **7 Conclusion**

Using rich data from over 60 countries, this paper has investigated how financial structures affect the dynamics and concentration of the export sector. While confirming prior findings that financial and banking development can boost the expansion of the export sector, the

> 22As noted by Abiad et al. (2008), entry barriers can take the form of outright restrictions on the participation of foreign banks; restrictions on the scope of banks’ activities; restrictions on the geographic area where banks can operate; or excessively restrictive licensing requirements.

21
visits and CD4 counts, the data includes all outcomes observed between recruitment into the study and completion of the FEATS experiment. The advantage of the latter approach is that the statistical power of the analysis increases dramatically, because observations included in the analysis originates from patients’ records and is therefore not dependent on a patient interview having been conducted with the said FEATS study participant.) The analysis conducted in this study comprised the following: _Firstly_ , we assess balance at baseline in adherence outcomes and other observable socio-demographic characteristics, by original intent-to-treat status and actual treatment arms (Tables 2-5). On the basis of this evidence, we reflect on the presence of potential selection bias. _Secondly_ , we employ basic bivariate analysis to assess differences in our main outcomes, i.e. self-reported adherence outcome, timeliness of clinic and hospital visits, and immunologic response, across treatment arms for intent-to-treat and actual treatment status (Tables 6-7; Figure 7-8). The statistical significance of differences in means and medians are assessed using t-tests, F-tests and Wilcoxon rank-sum tests. _Finally_ , we employ fixed effects (FE) linear regression models with Instrumental Variables (IV) and random effects (RE) linear regression models adjusted for selection bias to investigate the impact of the FEATS experiments on self-reported adherence, timeliness of clinic and hospital visits, and immunologic response to ARV treatment (Tables 8-10; Figure 9). In each case, we control for basic sociodemographics and ARV treatment duration. Following suggestions by Angrist et al (1996) and Greenland (2000), the original intent-to-treat status is used as instrument to adjust for selection bias in the fixed effects (FE) regression analysis, while in the random effects (RE) regression analysis the inverse Mills ratio is used to adjust for selection bias. To be deemed significant for the purposes of discussion, results need to be statistically significant at least at the 95% level (although results significant at a 90% level are reported).

# **4. Results**

At baseline, age, sex, education, monthly income and pre-ART CD4 count, in terms of the intentto-treat comparison, did not differ significantly across treatment arms, as did all self-reported measures of adherence. However, differences in marital status are statistically significant when comparing the peer adherence support only and control groups. The peer adherence support only arm comprise a larger proportion of single adults compared to the control group.

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Policy Research Working Paper 6764

# **Abstract**

This paper employs decomposition methods to analyze differences in agricultural productivity between male and female land managers in Ethiopia. It employs data from the 2011–2012 Ethiopian Rural Socioeconomic Survey. An overall 23.4 percent gender differential in agricultural productivity is estimated at the mean in favor of male land managers, of which 10.1 percentage points are explained by differences in land manager characteristics, land attributes, and unequal access to resources (the endowment effect). The remaining 13.4 percentage points are explained by unequal returns to productive components, but cannot be easily tied to specific covariates. These results are mainly driven by non-married female managers (mainly single and divorced). Married female managers do not display such disadvantages. Further analysis along the productivity distribution reveals that gender differentials are more pronounced at mid-levels of productivity and that the share of the gender gap explained by the endowment effect declines as productivity increases. Detailed decomposition of estimates at selected points of the agricultural productivity distribution provides valuable information for policy intervention purposes.

This paper is a product of the Poverty Reduction and Economic Management Unit, 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 arturo.aguilar@itam.mx.

_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
a very substantial role for official creditor financing of government spending. This will be important for the strength of the first-stage relationship between changes in predicted disbursements on loans from official creditors and changes in government investment.<sup>11</sup> Our main empirical results are in Tables 1 and 2, which report estimates of _β_ and for _βy_ obtained through a number of different variants on our basic empirical specification. Consider first Table 1, which presents estimates of the response of private investment to government investment. The first three columns report results for the full set of 916 annual country-year observations, while the second three columns report results for the subset of these countries located in Sub-Saharan Africa. Panel A reports ordinary least squares (OLS) estimates, Panel B reports two-stage least squares (2SLS) estimates, and Panel C reports the first-stage regression of changes in government investment on changes in the predicted disbursement instrument.

In the full sample of observations and in the Sub-Saharan Africa sample, we find OLS estimates of _β_ that are negative, small, and insignificantly different from zero (columns (1) and (4)). The 2SLS estimates are substantially larger, in the vicinity of 2. As expected given the importance of official creditor financing of government investment, the first-stage relationship between changes in government investment and changes in predicted disbursements is positive, and highly significant, particularly in the full sample where the first-stage F-statistic is 13.9. On the other hand, the relationship is weaker in the Sub-Saharan Africa sample, possibly due to greater measurement error in the government investment data, with the first-stage F-statistic of 7.3 falling short of the rule of thumb of 10. We therefore base inferences on weak-instrument consistent confidence sets reported at the bottom of the table. These are unfortunately quite wide, but exclude zero, suggesting a

> 11Interpreting these ratios is complicated by the fact that we do not observe the extent to which individual loans finance government investment versus government consumption. However, the scale of disbursements on these loans clearly is large relative to overall government spending.

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# Figure 1: The nal good sector

<!-- Start of picture text --> Domestic producers-H Exporters-H Productivity-H<br>0 40 0.4<br>-1 30 0.3<br>-2 20 0.2<br>-3 10 0.1<br>20 40 60 80 100 120 20 40 60 80 100 120 20 40 60 80 100 120<br>Price in H -H Price in F -H Real exch. rate<br>-0.2<br>1.2 -0.1 -0.4<br>1 -0.6<br>-0.2<br>0.8 -0.8<br>0.6 -0.3 -1<br>-1.2<br>0.4 -0.4 -1.4<br>20 40 60 80 100 120 20 40 60 80 100 120 20 40 60 80 100 120<br>Domestic producers-F Exporters-F Productivity-F<br>40<br>-1 0.6<br>30<br>-2<br>20 0.4<br>-3<br>-4 10 0.2<br>20 40 60 80 100 120 20 40 60 80 100 120 20 40 60 80 100 120<br>Price in H -F Price in F -F<br>1.5<br>0.4<br>TL<br>1<br>0.2<br>0.5 TL&TR<br>0<br>20 40 60 80 100 120 20 40 60 80 100 120<br><!-- End of picture text --> Note: H and F indicate respectively the Home country (i.e. the developed country) which is represented by a solid line and the Foreign country (i.e. the emerging country) which is represented by a dotted line. The blue lines display the dynamics with only trade liberalization, and the red lines display the dynamics when the tax reform is implemented.

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# **Figure 4. Government debt in small states and other EMDEs**

_Following a gradual rise since 2011, the average government debt-to-GDP ratio in small states increased sharply at the onset of the COVID-19 pandemic. While it has since declined, it remains higher than before the pandemic and in other EMDEs. Increases in debt among small states have been widespread. Small states rely more heavily on official borrowing than other EMDEs and have lower debt-carrying capacity, with debt risk ratings triggered at lower average debt levels._

## **A. Government debt**

## **B. Shares of small states and other EMDEs with high public debt**

<!-- Start of picture text --> Percent of GDP Percent<br>Small states Small states<br>120 EMDEs excl. small states 70 EMDEs excl. small states<br>60<br>100<br>50<br>80<br>40<br>60<br>30<br>40<br>20<br>20<br>10<br>0<br>0<br>2011 2019 2020 2023<br>2011 2019 2023<br>C. Composition of external public debt by D. Debt level by debt sustainability rating<br>creditor<br>Percent Private Multilateral Percent Small states<br>Paris Club Non-Paris Club 120 EMDEs excl. small states<br>100<br>100<br>80<br>80<br>60<br>40 60<br>20<br>40<br>0<br>20<br>2011 2019 2020 2022 2011 2019 2020 2022<br>Small states EMDEs excl. small 0<br>states Moderate High In distress<br><!-- End of picture text --> _Sources_ : International Debt Statistics (GDDS database); International Monetary Fund; WEO (database); World Bank.

_Note_ : EMDEs = emerging market and developing economies.

A. Average government debt to GDP ratio for 32 small states and 111 other EMDEs. Whiskers indicate interquartile range.

B. Bars show the percentage of small states and other EMDEs with debt to GDP ratios of at least 60 percent for 32 small states and 111 other EMDEs.

C. Average share of public and publicly guaranteed external debt by type of creditor for 22 small states and 100 other EMDEs.

D. Average debt as a percent of GDP in 2023 for countries classified at different levels of debt risk ratings, based on the published Joint World Bank-International Monetary Fund Debt Sustainability Framework for Low-Income Countries as of March 30, 2024. Sample includes up to 19 small states and 48 other EMDEs.

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**Table S1.3: Summary of estimated financial education impacts**

|Outcome||Significance|at 5%|Si|gnificance at|10%|Average<br>effect<br>size|
|---|---|---|---|---|---|---|---|
||_Negative_|_Insig._|_Positive_|_Negative_|_Insig._|_Positive_|(SE)|
|_A Effects o_|_n financial_|_literacy_||||||
|Fin.|1|72|117|2|62|126|0.263***|
|literacy|(0.53%)|(37.89%)|(61.58%)|(1.05%)|(32.63%)|(66.32)|(0.414)|
|_B Effects o_|_n financial_|_behavior_||||||
|Fin.<br>behavior|8<br>(2.29%)|215<br>(61.60%)|126<br>(36.10%)|18<br>(5.16%)|181<br>(51.86%)|150<br>(42.98%)|0.086***<br>(0.012)|
|_Borrowing_|5<br>(5.00%)|80<br>(80.00%)|15<br>(15.00%)|10<br>(10.00%)|70<br>(70.00%)|20<br>(20.00%)|0.023<br>(0.014)|
|_Budgeting_<br>_& planning_|0<br>(0.00%)|15<br>(37.5%)|25<br>(62.50%)|1<br>(2.50%)|10<br>(25.00%)|29<br>(72.50%)|0.207***<br>(0.053)|
|_Saving_|2<br>(1.67%)|61<br>(50.83%)|57<br>(47.50%)|6<br>(5.00%)|49<br>(40.83%)|65<br>(54.17%)|0.108***<br>(0.017)|
|_Retirement_<br>_Saving_|0<br>(0.00%)|22<br>(47.83%)|24<br>(52.17%)|0<br>(0.00%)|17<br>(36.96%)|29<br>(63.04%)|0.108***<br>(0.034)|
|_Insurance_|0<br>(0.00%)|13<br>(81.25%)|3<br>(18.75%)|0<br>(0.00%)|12<br>(75.00%)|4<br>(25.00%)|0.045<br>(0.024)|
|_Bank_|0|10|1|0|10|1|0.003|
|_account_<br>_behavior_|(0.00%)|(90.91%)|(9.09%)|(0.00%)|(90.91%)|(9.09%)|(0.027)|
|_Remittance_|1|14|1|1|13|2|0.035|
|_behavior_|(6.25%)|(87.50%)|(6.25%)|(6.25%)|(81.25%)|(12.50%)|(0.046)|

Notes: Average effect sizes are estimated via OLS with standard errors clustered at the study-level in parentheses. ***, ** and * denote significance at the 1%, 5% and 10% level.

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We use the estimation results from the full specification including vaccination proxies (reported in table A5) for the first set of simulations. The procedure is straightforward. In the first simulation we assume that all mothers complete at least primary schooling (which means that the schooling status of uneducated mothers is “upgraded” in the data base), then we recompute the mortality risk for each child using the estimated coefficients of the survival models and finally derive the corresponding U5M rate at national levels. The child mortality reductions obtained through this procedure are driven both by the values of the coefficient related to mother’s education in the survival models and by the initial rates of primary education completion, i.e. the number of uneducated women who, in the simulation, are endowed with primary schooling. In other words, policy leverage depends on the effectiveness of the intervention in each individual case as well as the (additional) number of mothers and children who benefit from the respective intervention. We repeat the same exercise for the full elimination of short birth intervals and full BCG vaccination coverage. Table A13 in the appendix illustrates the scope of the respective simulated policy changes. The results of the three simulations are reported in Table 2 alongside the reduction needed to achieve MDG4 to put those simulated reductions in perspective.<sup>10</sup> While descriptive statistics show that both levels and progress in mothers’ education across our sample countries are very uneven, our estimation results certainly indicate that it is an important determinant of child mortality through its impact on birth intervals and vaccination status. With renewed efforts in the area of primary education, improvements are expected to be larger in the course of the next 10 to 20 years in most of the countries. Nevertheless, we are of course aware that Guinea, Mali, Niger, and Mozambique are unlikely to reach 100 percent completion rates among young mothers. Yet, considerable improvements have been made recently in most countries. In Guinea, Mali, and Mozambique the enrollment rate now stands between 50 and 60 percent. In this group, only in Niger enrollments remains comparatively very low with only around 30 percent. Current enrollment rates in the other countries also suggest considerable improvement in mothers’ education although full achievement of MDG 2 might not be reached.

> 10 As the residual in the underlying binary choice model (logit) cannot be observed, this exercise involves the simulation of a residual that is consistent with the observed value of the left-hand side variable, i.e. 0 for survival and 1 for death.

35