rafmacalaba/datause-encoder-data huggingface dataset:
Table III.2: Total long-term debt _(%_ of GNP)

||1980*<br>|1985<br>|1988<br>|
|---|---|---|---|
|Intensely adjusting countries (IAL)|35.1|82.4|82.9|
|<br>Other adjusting countries (OAL)|32.1|_51.5_|70.6|
|Nonadjusting countries (NAL)|27.1|57.6|53.1|

'1980 figures refer to public long-tern! debt only.

Source: World Development Reports (1982, 1987, 1990).

Table III.3: Education financing (%o of discretionaxy public expenditures)

||Countries|1975|1980|1986|
|---|---|---|---|---|
|EIAL|10|16.9|17.6|16.2|
|OAL|_5_||11.7|12.7|
|NAL|_5_|8.7|10.9|12.9|

Source: World Bank, 1990a.

3.04 Why did education's share of the budget decline in adiusting countries? One explanation is that debt service payments crowded out other public spending. Countries that adopted adjustment programs (IAL and OAL countries) already faced higher debt burdens in 1980 than did nonadjusting countries (table I.2). Since over 90 percent of long-term debt is public in all developing country groups,y nonconcessionl adjustment borrowing increased the debt burden and contributed to a resource transfer toward debt payments and away from other publicly financed sectors. The debt burden increased during the 1980s for all country groups, but the increase was more rapid for adjusting countries, particularly for intensely adjusting countries. At the same time, GDP growth (and government revenues) stagnated or declined in many countries. Table 111.3 suggests that governments "protected" education during the early 1980s--education's share of the discretionary public budget (i.e., excluding debt service payments) increased slightly. However, in intensely adjusting (EIAL) countries, the growing debt burden after 1980 outweighed attempts to shelter the education budget. Box 111.2 describes the case of Tanzania, an intensely adjusting country. Finally, currency devaluations may induce shifts in the allocation of public resources towards sectors that are more import- or foreign exchange-intensive (including debt service) than education.

3.05 A second explanation for education's declining public budget share is that a change in relative prices in turn alters relative resource allocations, independent of any concrete reallocative decisions. For example, because education is a relatively laborintensive sector, any across-the-board civil service wage cut will, ceteris paribus, reduce education's share of the public budget relative to less labor-intensive public sectors (Schwartz and Stevenson, 1990).

2/Calculated from World Debt Tables.

23
|(I2) Female x Urban|-|+*|-|+|-|
|---|---|---|---|---|---|
|(I3) = (I1) + (I2)|+***|+***|-|+*|-|
|(I4) Post-COVID x Urban|-***|-|-|-|+|
|(I5) Female x Post-COVID x Urban|+***|-|+|+|-|
|(I6) = (I4) + (I5)|+|-|+|+|-|
|(I7) = (I1) + (I4)|-|-|-|+|+|
|(I8)=(I1)+(I2)+(I4)+(I5)|+***|+|+|+|-|
|(J1) Above-median assets|+***|-**|+|+**|+***|
|(J2) Female x Above-median assets|-|+|+|+|+|
|(J3) = (J1) + (J2)|+***|-|+|+***|+***|
|(J4) Post-COVID x Above-median assets|+|+**|+|-**|-|
|(J5) Female x Post-COVID x Above-median<br>assets|-|-|-|-|+|
|(J6) = (J4) + (J5)|-|+|-|-***|-|
|(J7) = (J1) + (J4)|+***|+|+|-|-|
|(J8)=(J1)+(J2)+(J4)+(J5)|+|+|-|-|+|
|Observations|12,027|7,686|1,633|6,409|6,255|
|R-squared|0.171|0.093|0.245|0.195|0.163|

Notes: Summary of regression results from regressions estimating the probability of attending school as a function of individual and household determinants, and their interactions with a post-COVID round indicator and a female indicator. Positive signs (+) indicate an associated increase, negative signs (-) a decrease in school attendance probabilities. Statistical significance: *** p<0.01, ** p<0.05, * p<0.1. Pre-COVID round from LSMS, post-COVID data from HFPS respectively for all countries. The sample consists of 16-18 years old adolescents for Malawi, and 10-18 years old adolescents for all other countries.

# **6. Conclusion**

The COVID-19 pandemic has been an unprecedented global shock that has highlighted the vulnerability of systems and people to interconnected shocks and raised important questions about countries' readiness to face future pandemics. In this paper, we investigate how the pandemic has affected the school attendance of adolescent girls and boys in Sub-Saharan Africa. Our aim is to deepen the understanding of the impact of global shocks and draw lessons that can inform efforts to prevent or mitigate the negative effects of future pandemics.

Our study provides important insights into the gendered impacts of the COVID-19 pandemic on adolescents' school attendance in Sub-Saharan Africa. While we did not find evidence of increased education gender gaps during the pandemic, the erosion of traditionally protective factors, such as descent from household head, parental education, and household wealth, is concerning. For instance, being a descendant of the household head, which was previously correlated with a higher probability of school attendance among adolescents, lost its predictive power after the pandemic, indicating that family support for education may have weakened. Some adolescents may have had to forgo school attendance due to competing demands on household resources, such as providing additional caregiving support, or engaging in income-generating activities.

23
JAPAN: The IMR and life expectancy at birth are averages for 1986, 1987, and 1988 from vital registration data (Institute of Population Problems, Ministry of Health and Welfare, 'Ibkyo).

KAMPUCHEA, DEM.: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._

**KIRIBATI:** Both the IMR and life expectancy at birth are Bank projections from estimates for 1989 (U.S. Bureau of the Census, 1989).

KOREA, DEM. PEOPLE'S REP.: The IMR and life expectancy at birth are from _World Population Prospects 1990._ Life expectancy at birth estimates are based on vital registration data through 1987, adjusted for underregistration. The IMR is from the U.N. "Far Eastern" model life table selected on the basis of estimated life expectancy at birth.

KOREA, REP. **OE:** The IMR and life expectancy at birth are from _World Population Prospects 1990._ Life expectancy at birth estimates are based on the CDR from the 1987 Continuous Demographic Survey and the sex-age pattern of mortality in the official life table for 1978-79. Infant mortality is based on the estimated life expectancy at birth. LAOS: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ These estimates are based on the 1986 and 1988 Demographic Survey, and Coale-Demeny "West" model life tables.

MACAO: The IMR is average of official estimates for 1985 to 1989 as published in _PVSR_ (10/8710/90). Life expectancy at birth is from Coale-Demeny "West" model life table selected on the basis of the IMR.

MALAYSIA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ They are estimated from vital registration for Peninsular Malaysia through 1985 and official government estimates for Sabah and Sarawak in 1980 and projected to 1985-89.

MALDIVES: The IMR is a Bank projection of an estimate provided by ESCAP (1988); it is consistent with the indirect estimate calculated from the 1990 census. Life expectancy at birth is the corresponding Coale-Demeny "West" model life table estimate and is consistent with the 1990 census indirect estimate.

MONGOLIA: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ Life expectancy at birth is based on official estimates of the CDR through 1986 and a United Nations estimate of a general pattern model life table. The IMR is based on the United Nations general model life table and life expectancy at birth.

MYANMAR: Both the IMR and life expectancy at birth are from _World Population Prospects 1990._ These estimates are based on analysis of the 1983 census, projected with the U.N. medium decline scenario to 1985-89.

NEPAL: The IMR and life expectancy at birth are from _World Population Prospects 1990._ The IMR is based on the Demographic Sample Survey of 1986/87 and projected with the U.N. medium decline model. Life expectancy at birth estimates are from corresponding model life tables.

NEW CALEDONIA: The IMR and life expectancy at birth are Bank projections from estimates for 1989 (U.S. Bureau of the Census, 1989).

13
at least consistent with the reductions in primary-school-age child employment (under 14) that we observe in Table 2. We find highly significant reductions in primary school enrollment rates with exposure to child labor provisions. Our estimates range from 0.3–1.2 percentage point drops which correspond reductions in primary school enrollment by 0.3%–1.3% of the mean. These findings are consistent with our weak, statistically insignificant findings from Table 2 which suggests that under 14 employment increases following exposure to child labor provisions in RTAs.

Two points on these primary school results warrant further discussion. First, primary school enrollment rates are unlikely to suffer the same misreporting issues that direct enumeration of under-14 workers in our survey microdata might suffer from. Consequently, these more precisely estimated drops in primary school enrollment rates might reflect stronger employment effects than we are able to directly identify with under-14 employment data. Second, primary school enrollment results might reflect under-14 labor market effects at both the intensive and extensive margin. Our child employment rates capture changes in child labor only at the _extensive_ margin—entry and exit from employment. But there may also be intensive margin effects of child labor provisions in RTAs, in which under-14 children who are employed end up working more hours on average. Such intensive margin effects would not be reflected in changes in country-level under-14 employment rates but may be reflected in lower primary school enrollment rates as work and school together become unfeasible.

We find similar patterns with secondary school enrollment rates in Panel B of Table 3, however the income effects from trade liberalization are now statistically and economically significant in some specifications. We estimate increases in secondary school enrollment rates of up to 3% of the mean following entry into force of RTAs without child labor provisions. As above, Columns (1)–(3) report models with country-RTA fixed effects and no time fixed effects, event time fixed effects, and calendar year fixed effects, respectively. Columns (4)–(6) report analogous models using our balanced, backward interpolated panel and Column (7) reports synthetic DiD estimates. Inclusion of child labor provisions reduces secondary school enrollment rates by 0.2–0.9 percentage points, up to 1.2% of the mean. These findings are significant across a number of specifications, notably at the 5% level in our synthetic DiD specification. In general, these findings lend credence to our findings 22
4 2. The influence of mortality on fertility, at first debated widely, has cown to be accepted, based both on micro level household studies and macro studies using aggregate datal. Similarly the direct influence of fertility 2 on mortality is equally well established. Recognition of the simultaneous interaction came only later but is now also accepted although statistical estimates of the simultaneous effects, using either household or aggregate data, have been few<sup>3</sup> , and there have been no ap.plications of the empirical 4 analyses to policy assessment. The present paper fills a gap by providing lIn their survey of the determinants of fertility McGreevey and Birdsall (1974) cite studies estimating elasticities of .2 to .4 for the .1sponse of completed fertility to changes in infant mortality (1974, p41, Cchrane and Zachariah (1983) survey a number of studies and also reexa. Torld Fertility Survey data using linear regression. They conclude, base-d on an average for 25 countries, that the effect of child mortality on stusequent fertility is about .5. Some other studies establishing the effect of infant or child mortality on fertility in individual countries are Zachariah and Patel (1984), Schultz (1978), and Preston (ed., 1978).

2Much of the literature looking at the effects of fertility on mortality is focussed on the relationship between birth spacing and child survival. In a cross country study by Pebley and Millman (1986) it is found that the risk of dying for a child born with less than a two year birth interval is approximately 60% greater in the neonatal period and 100% greater in the remainder of the infant period than for a child born after more than a two year blrth interval. Also see Cleland and Sathar (1983), and Hobcraft (1984). There have also been studies looking directly at the effects of fertility on infant mortality. See, for example, Scrimshaw (1979), and Taucher (1982).

3The earliest estimates of the interaction of fertility and mortality rates are those by Nerlove and Schultz (1970) explaining fertility and infant mortality in Puerto Rico, and Heller (1976) in a simultaneous equations model explaining infant mortality and age specific fertility rates in Malaysia. Using cross country data, Wheeler (1980) estimated a set of simultaneous equazions relating various components of a "basic needs' model, including equations explaining fertility rates and life expectancy.

4The potential importance of the interaction has been examined in simulation models for Nepal by Barnum and Mohtadhi (1982) and Barnum and Yaukey (1979), and in a survey paper by Gwatkin (1984). Cochrane has also noted that the policy implications of family planning programs are significantly influenced by the effects of mortality on fertility (1983).
resource booms may present for developing countries, and what, if anything, macroeconomic management can do to overcome them.

We find that the “missed opportunity” was not universal. In fact, several resource-rich countries have been able to sustain relatively higher economic growth beyond the resource boom. Those who performed best were also those who saw relatively higher savings and investments rates out of resource revenues – for example those with higher levels of Adjusted Net Savings. While resource extraction implies a process of asset depletion, the high performing countries were typically those who accumulated assets in other categories of national wealth, including physical capital, human capital and even other forms of natural capital, such as forests, fisheries and natural ecosystems.

# **Key Findings**

Sub-Saharan Africa has seen rising degrees of resource dependence since 2004. First, the number of resource-rich countries rose considerably during the boom years, from 18 to 26 according to the IMF (2012) definition using 20% of exports or 20% of government revenues.<sup>3</sup> Overall natural resource exports increased their share of total merchandise exports; rising from an average of 23 percent between 1998 and 2003 to 28 percent on average during the boom years of 2004-2014.

The boom period also saw a sharp increase in the levels of resource revenues flowing to the government. During this period resource-rich African countries’ resource government revenues grew by an average of 1.1 billion US$ each year, from an average of 8.5 percent of GDP during the pre-boom years to 8.6 percent of GDP. This represents about 61% of total government revenues. At the same time, average resource revenues share in GDP in resource-rich countries were almost four times greater than in non-resource-rich SubSaharan Africa countries (2.1%) but around 7 percentage points lower than other nonresource-rich countries elsewhere in the world, standing at around 15.5% (Figure 1). Some countries stand out in particular. Natural resource revenues flows were considerably higher in Angola, the Republic of Congo, and Equatorial Guinea, where the measures of resource dependency in resource revenue terms rose to more than 25 percent of GDP (Figure 2).

> 3 For the remainder of this paper, this group of 26 resource-rich countries who exceed this ‘resource-richness’ threshold will be compared to the other 22 counties in the region who do not throughout this analysis. Second, when viewing aggregates for the entire region we see a rise in resource dependency for the region as a whole.

4
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|**Data Pro**|**perties**||||**M**|**odeling App**|**roach**|||
|---|---|---|---|---|---|---|---|---|---|
|**Sample**<br>**and**|**Frequency**|||**Type**||||||
|**Author (year)**<br> <br>**Source**||**Variables**|**Integration**||**Specification**||**Estima**|**tes**||
|1995Q1-<br>2007Q4|1995Q1-<br>2007Q4 are<br>|real money<br>balances,<br>|_ln_Yt_and<br>_ln_Et_are||_Et+ b4 Rt +_<br>_b5 CPIt-1 +_<br>|C|3.3065|2.9401|0.005|
|IMF -<br>|not available<br>for Egypt<br>either in this<br>source or any<br>other source,|(_ln_RM2t_),<br>real GDP<br>(_ln_Yt_), FX<br>rate (_lnEt_)|integrated<br>of order<br>one, i.e. I~<br>(1) both_Rt_||_et ._|LN_Y|0.70235|8.2304|0.000|
|IFS|we used<br>statistical<br>methods to<br>extrapolate<br>quarterly data|___,<br>three-month<br>deposit rates<br>(_Rt_) and|, <br>and_EPt_<br>are<br>integrated|||LN_E|0.4470|11.209|0.000|
||<br>from the<br>annual data<br>for the period<br>of 1995-|lagged CPI-<br>inflation rate|of order<br>zero, i.e., I~<br>(0).|||R|-0.0400|-4.4905|0.000|
||2007.|||||EP|-0.0105|-2.3769|0.021|
|||||||AR(1)<br>Adjusted R<br>Akaike inf<br>Durbin-Wa<br>criterion -4<br>F-statistic 1<br>Prob (F-sta|0.2763<br>-squared 0.99<br>o criterion -4.3<br>tson stat. 2.03<br>.147432<br>145.690<br>tistic) 0.00000|1.9020<br>1511<br>76875<br>1932 Schwa<br>0|0.063<br>rz|

95
vignette responses amongst the regressors commonly used to explain subjective welfare. However, the effects were neither very strong nor consistent across different vignettes.

In their second and third tests, BHR explored the extent of bias due to the aforementioned frame of reference effect by embedding vignette rankings by households in standard regressions for subjective welfare. BHR did this in two ways: they included vignettes among the covariates and, to address endogeneity concerns with this approach, BHR also recalibrated the selfassessments of welfare to accord with the heterogeneity in scales revealed by the vignette responses. The striking finding of these tests is that the bias is negligible with respect to the ―income effect‖ on subjective welfare as well as other covariates. Based on this study, the concerns that past uses of subjective economic welfare data are compromised by systematic differences in the meaning given to the scales used appear to be unwarranted.

A third test by BHR involved estimating an explicit model of the heterogeneity in scales, whereby the thresholds are modeled as functions of covariates, assuming that the structure of this heterogeneity is common between ―own welfare‖ and the vignettes.<sup>34</sup> As usual, there is assumed to be a continuous latent variable for the respondents’ self-rated welfare, but now the thresholds are explicit functions of a vector of explanatory variables. The extra information on the vignette responses provides the basis for identification, under the assumption that the thresholds for selfassessed welfare are determined identically as for these thresholds in the vignette responses. This is a natural assumption to make. Without the vignettes, identification would only be possible under questionable assumptions about the nonlinearity of the functional forms involved (as discussed in King et al., 2004). Thus BHR were able to model determinants of the thresholds separately to those of the latent continuous variable for subjective welfare.

This approach revealed similar results on the factors influencing subjective welfare, but also threw new light on the heterogeneity in scales. In particular, BHR found that poorer households tend to have a narrower range in the thresholds used in identifying where they lie and where the vignettes lie on the subjective welfare ladder.

> 34 Here BHR followed an approach developed by King et al., (2004) who proposed an econometric method (―CHOPIT‖) for modeling the same type of data use by BHR in which there is an equation for both the latent response of interest and one for the vignettes, with the two equations estimated jointly.

26
It may also be of interest in comparing our point estimates to other studies where the preschool ages are between age 3 and 5. Appendix Table 4 is an analogue to Table 2 but changes the age eligibility to be between 3 and 5. Our findings are consistent with this incremental change. In Column 5, suggesting that an additional public preschool per 1,000 children increases women’s work participation by 6.6 percentage points per preschool-aged eligible child.

Appendix Table 7 explores whether our findings on sectoral changes hold, conditional on employment. We do not find evidence that women switch occupations as a result of having better access to public or private preschools, conditional on being employed. Reconciling this with Table 7 suggests that women with eligible children are more likely to work and, if they work, they work in occupations that do not require full-time commitment. However, once they work, they are not more likely to switch to any other occupation. Appendix Table 8 does the same thing for industry choice.

The estimated preschool availability effects on maternal employment are also robust to dropping large population districts. Population is defined based on the population in 1996 to minimize possible sorting to better preschool access districts.<sup>24</sup> Appendix Table 11 summarizes our findings with DDD (column 1-3) and DDD-FE (column 4-6) strategies. Columns 1 and 4 are the most restrictive, examining districts with less than 0.5 million population in 1996. Columns 2 and 5 include districts with a population of up to 1 million in 1996, while columns 3 and 6 include a population of up to 1.5 million. Coefficients should be compared to columns 5 and 6 in Table 3. While the positive effect of public preschools and null effect of private preschools generally remains, it is worth noting that effect sizes do get smaller in smaller districts. The interaction coefficient of public preschools is no longer statistically significant in districts with a population of less than 0.5 million with the DDD strategy and only statistically significant at the 10 percent level with the DDD-FE strategy. This potentially suggests that preschools may be more beneficial to mothers living in highly populated districts with better economic opportunities.

# **_6.7 Sorting to better preschool access_**

To check if mothers with preschool-aged children sort into districts with high preschool access, we aggregate our constructed panel of individuals over the years to a panel of district-year.<sup>25</sup> We

> 24 1996 is the earliest round of SUSENAS, with complete coverage of the 290 districts.

> 25 By construction, one PODES year is matched to several years in the panel of mothers. For instance, PODES 1993 is assigned to year 1991-1993 in the panel of mothers. If the time unit is defined annually, we obtain, mechanically,

27