rafmacalaba/datause-encoder-data huggingface dataset:
five countries in Eastern Europe where crisis response survey data are available. Finally, Section 6 summarizes the main findings and concludes.

# 2 Literature on the impact of income shocks on human capital investments

One of the most common strategies adopted by households to cope with income shocks is to reduce household expenditures (Azam, 2010; Chambers, 1989; Fiszbien, Giovahnoli, and Aduriz, 2003; World Bank, 2011). These expenditure reductions take many forms: substituting cheaper goods, delaying consumption of durable goods, reducing human capital investments by withdrawing children from schools, reducing visits to doctors, or reducing medicine and medical care utilization. Some of these coping strategies may lead to lower human capital accumulation and subsequently, lower lifetime earnings (see Case, Fertig and Paxon, 2005; Hoddinott et al. 2008; Ferreira and Schady, 2008). The choice of coping strategies, however, vary extensively across households depending on their social and economic status and location (Corbett,1988). Household human capital endowments also play a key role. Lokshin and Yemtsov (2001) find that during 1998-99 economic crisis in Russia, households with higher human capital choose strategies that included increasing home production and moving the place of residence to cope as compared to households with lower human capital endowments.

The impact of income shocks on human capital investments, especially education, is heterogeneous (UNESCO, 2000; Skoufias, 2003). Jacoby and Skoufias (1997) find that school attendance by rural poor children in India decreases as a consequence of adverse income shocks. Duryea (1998) for Brazil and Skoufias and Parker (2002) for urban Mexico, both find similar adverse effects on school attainment<sup>5</sup> . Evidence from Central and Eastern Europe and the Commonwealth of Independent states shows that the economic transition led to a reduction in enrollment (Paxon and Schady, 2005).

However, Mckenzie (2003) finds that school attendance rates rose among 15-18 year olds

> 5 See World Bank (2011).

4
**Table 3. Regression results showing the difference in total monetary consumption per capita between the non-lean season and different months in the lean season**

||Burkina Faso|Niger|Senegal|All except Chad and<br>Mali|
|---|---|---|---|---|
|April|-0.0263|-0.0253|-0.1441***|-0.0908***|
||(0.0409)|(0.0418)|(0.0297)|(0.0237)|
|May|-0.1428***|-0.0657**|-0.0556***|-0.0942***|
||(0.0321)|(0.0309)|(0.0208)|(0.0175)|
|June|-0.1579***|-0.0714**|-0.0865***|-0.1027***|
||(0.0338)|(0.0281)|(0.0283)|(0.0181)|
|July|-0.1793***|-0.1549***|-0.0780|-0.1613***|
||(0.0523)|(0.0272)|(0.0826)|(0.0266)|
|N|6936|5919|7107|19962|
|R-squared|0.5486|0.4738|0.5626|0.5861|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the ‘grappe’ level in parentheses. Dependent variable is the log of total monetary consumption per capita. Individual-level sampling weights applied. All specifications include controls for the gender, age, and education level of the household head, household size, the dependency ratio, household walls material, household roof material, household floor material, access to electricity, access to improved drinking water, and access to improved sanitation as well as an urban-rural dummy and region fixed effects. Source: EHCVM and World Bank estimates.

# 4.2. Disaggregated differences in consumption

We now disaggregate the results by different parts of the consumption basket to characterize more fully the deprivation implied by the overall changes in consumption and poverty described in Section 4.1. We begin by breaking down the results by food and non-food consumption; that is, we re-estimate Equation 1 with the log of food consumption per capita and the log non-food consumption per capita as the main regressors. Pooling the data from Burkina Faso, Niger, and Senegal, food consumption was 11.4 percent lower in the lean season wave while non-food consumption was 7.4 percent lower (Table 4). The difference between these two effects is even larger at 11.3 percent for food consumption and 6.3 percent for non-food consumption when full controls are included.<sup>23</sup> This in itself suggests that seasonality may have more effect on food consumption. Nevertheless, these results may partly be an artifact of the way in which the EHVCM data were collected; the recall period for the consumption of food items generally covered the previous seven days, while non-food items were recorded for the previous seven days, 30 days, three months, six months, and 12 months, which may be less sensitive to seasonal swings. It is therefore important to dig deeper into the consumption basket.

> 23 We use Stata’s `suest` command to formally test the difference between the effects of seasonality on food and non-food consumption. When pooling Burkina Faso, Niger, and Senegal, the p-value on the formal test for the difference between the food and non-food coefficients is 0.1190 without controls and 0.0003 with controls.

15
Table 1.15

||**TheDutch"Drain" on**Indon|esia,1698-1930|
|---|---|---|
||Indonesianexportsurplus<br>aspercentofIndonesian<br>netdomesticproduct|Indonesian exportsurplus<br>aspercentofDutchnet<br>domesticproduct|
|1698-1700|0.7|1.1|
|1778-80|0.9|1.7|
|1868-72|7.4|5.5|
|1911-15|7.6|8.7|
|1926-30|10.3|8.9|
|Source:Maddiso|n(2001:Table2-21a,87)||

According to the estimates in Table 1.15, the amount of Dutch income in Indonesia that was remitted out of the country jumped from 0.9 percent in the precolonial period (1778-80) to 7.4 percent in the early part of the colonial period (which started in 1815). This reached as high as 10.3 percent by 1926-30. This is despite the fact that the Dutch reduced the percentage of their Indonesian income remitted out of the country between 1870 and 1921 as the economy was opened up to private enterprise. Their income as a percentage of Indonesian domestic product had risen from about 1.4 percent in 1700 to about 17 percent in 1921-38.125 It can also be seen in the right column of Table 1.15 that Indonesian income added significantly to the Netherlands' domestic product by the late 1920s (8.9 percent). According to Maddison (1989), from 1840 onwards, this income made up the preponderance of Dutch foreign income.

Table 1.16

||**TheBritish"Drain"onInd**|**ia,1868-1930**|
|---|---|---|
||**Indian exportsurplusas**<br>**percentofIndiannet**<br>**domesticproduct**|**Indian exportsurplusas**<br>**percentofBritishnet**<br>**domesticproduct**|
|1868-72|1.0|1.3|
|1911-15|1.3|1.2|
|1926-30|0.9|0.9|
|Source: Maddis|on(2001: Table2-21b,87)||

Table 1.16 reveals that the "drain" on India was considerably less than that on Indonesia. British income in India was only about 5 percent in 1921-38 (versus 17 percent for the Dutch in Indonesia) and of this, only 1.7 percent was remitted.<sup>126</sup> Also, the addition of Indian income to Britain's domestic product was only 0.9 percent in 192630 compared to 8.9 percent for the Dutch (from Indonesia) in the same period.

26
**Table 9** <u>.</u> _<u>Prospera</u>_ <u>program impact on education and work outcomes</u>

||Educ|<br>ation|Work pa|<br>st week|<br>Mut|<br>uallyexclusi|In school<br> <br>ve combina|Neither in<br> <br>tions|
|---|---|---|---|---|---|---|---|---|
||Attends|Attends<br>regularly|Any work|Work for<br>pay|In school<br>only|In work<br>only|and in<br>work|school nor<br>in work|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|OLS only controlling for municipality and child age|0.058***|0.058***|-0.009|-0.010***|0.053***|-0.014***|0.005|-0.044***|
||(0.012)|(0.014)|(0.007)|(0.004)|(0.012)|(0.004)|(0.006)|(0.010)|
|Additional information:|||||||||
|Number of observations|10,821|10,805|10,886|10,867|10,774|10,774|10,774|10,774|
|Observations in control group, boys|4142|4135|4177|4171|4121|4121|4121|4121|
|Observations in treatment group, boys|6679|6670|6709|6696|6653|6653|6653|6653|
|Mean in control group, boys|0.841|0.805|0.051|0.028|0.827|0.035|0.015|0.124|
|Mean in treatment group, boys|0.900|0.863|0.042|0.018|0.880|0.021|0.020|0.079|

Note. Estimates of _Prospera_ program impact on education and work outcomes of children aged 10 to 14 from eligible households. Standard errors are clustered at the level of localities. *** p<0.01, ** p<0.05, * p<0.1 57
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 15. Benchmarking ICT indicators**

|||Angola<br>2000|Angola<br>2008<br>Lower-mid<br>income gr<br>2008|dle-<br>oup<br>Sub-Saharan<br>African region<br>2008|<br> <br>Angola 2009|
|---|---|---|---|---|---|
|GSM coverage|% population under signal|—|31<br>77|56|31|
|International bandwidth|bits/person|0.1|20<br>153|34|—|
|Internet|users/100 people|0.1|5.3<br>13.9|5.1|6.4|
|Landline|subscribers/100 people|0.5|0.6<br>13.6|1.5|0.7|
|Mobile phone|subscribers/100 people|0.2|39.9<br>28.5|33.3|48.5|
|_US dollars_|Angola<br>2005|Angola<br>2008|<br>Lower-middle-<br>income group<br>2008|Sub-Saharan<br>African region<br>2008|Angola<br>2009|
|Price of monthly mobile ba|sket<br>12.45|11.8|8.4|11.8|11.32|
|Price of monthly fixed-line|basket<br>—|21.1|4.8|11.6|20.3|
|Price of monthly fixed broa|dband<br>—|119|31.4|100|77|
|Price of a call to the United|States per minute<br>1.2|1.3|—|0.8|1.2|
|Price of an inter-Africa call|per minute<br>—|1.4|—|1.0|1.3|

_Source_ : Adapted from Angola Telecom, Inacom, Unitel, AICD, and World Bank ICT at-a-Glance. — = Not available.

# **Challenges**

Despite sector reforms, the actual level of competition remains low. Mobile competition is limited; the two operators providing service use two different technologies (CDMA<sup>4</sup> in the case of Movicel and GSM<sup>5</sup> in the case of Unitel), making it more costly for subscribers to switch networks because of the need to purchase new equipment. While there is a high _de jure_ level of competition in the fixed-line market, in reality, the new operators have delayed getting off the ground. Despite being licensed years back, they have only recently started to provide services. Movicel spun off from Angola Telecom in 2010, with 80 percent sold to investors (Almeida 2009). Meanwhile, Angola Telecom remains fully government owned. The impact of limited competition is apparent in pricing: Angola’s ICT tariffs are above both the SubSaharan African and lower-middle-income group averages (table 15, bottom).

Angola’s domestic backbone consists of microwave, VSAT,<sup>6</sup> and as of recently fiber-optic cable. The first phase of the national fiber-optic backbone linking 18 provincial capitals through 6,000 km of fiberoptic cable is scheduled to be concluded in 2011 ( _Macaohub_ , November 17, 2010). A notable feature of the domestic backbone is the 1,800-km ADONES portion stretching along the country’s Atlantic coastline (WFN Strategies 2009). Angola was one of the few African countries to obtain access to an international undersea fiber-optic cable when it connected to the South Atlantic 3 (SAT-3) cable in 2002. Despite this, Internet access prices have remained high, in part due to Angola Telecom’s monopoly over the cable. The country is slated to connect to two more cables in the near future: the Africa Coast to Europe (ACE) cable and the West Africa Cable System (WACS). A consortium made up of the country’s

> 4 Code division multiple access.

> 5 Global system for mobile communications.

> 6 Very small aperture terminal.

33
a tertiary-level degree as technicians or technologists. Some professions offer an additional certificate evaluation, which provides graduates with a professional license in their specialization. The public systems of TTCs offer 172 careers within 20 knowledge areas (see **Table A1** in **Appendix A** ).

Students who enroll in public TTCs come from low and medium-income households, and many cope with work and study simultaneously. Almost half of them come from families with parents who have achieved at most primary education. This population is more likely than the traditional college student to enter the system with academic gaps, especially in core foundational numeracy and literacy skills. Available data from the year 2021 revealed that about 61 percent of all new entrants to the public TCC system display inadequate levels of core competencies necessary for college readiness (such as communications, numeracy, and problem-solving) and were at risk of not being able to complete their post-secondary education successfully (ACET, 2021).

Inadequate academic readiness often curtails student academic progression. For instance, in the first semester of 2018, approximately 19.6 percent of first-year students enrolled in public TTCs dropped out after six months, whereas 33 percent dropped out after 12 months (or two academic semesters).

# _3.2. Technical Higher Education Provision Under COVID-19_

The COVID-19 pandemic led to the closure of in-class instruction in technical institutes nationwide and the adoption of remote learning modalities that began in March 2020 and continued for the academic period in 2021. In-person classes were gradually reintroduced to students starting in March 2022. Adopting virtual learning modalities was abrupt. The SENESCYT had to revise the admission requirements for public higher education students. Traditionally, all students who completed high school were required to take the " _Ser Bachiller_ " exam, an assessment designed to evaluate high-school graduates' knowledge in mathematics, language and literature, natural sciences, and social sciences. During COVID-19, the Ministry of Education canceled the exam. Using similar content items and those in the " _Ser Bachiller_ " assessment, the SENESCYT developed a new exam (the EAES, or exam for seeking access to higher education) required for students who wanted to enroll in public higher education, including TTCs.

5
_1990,_ the projections are made by the U.N. Population Division with methodology discussed below; otherwise they a.- Bank projections.

World Bank methodology for projecting mortality indicators is described in detail in Bulatao and Bos (1989). The IMR and life expectancy are projected separately. Life expectancy at birth for males and females separately is projected from previous trends and from socioeconomic factors. Life expectancy at birth is projected from year 0 to year t using a logistic function over time of tCe form et = ko + k / {1 + exp[logit(eo) + rt]}, with logit(eo) = _1og,_ [(ko + k - eo) / (eo - ko)].

The logistic function is set to rise most rapidly from a level of 50 years or so and increasingly slowly at higher levels. The minimum (ko) for the logistic functions for both sexes is assumed to be 20 years, and the maxima (ko + k) are assumed to be 90 for females and 83.3 for males. The rate of change (r) for the logistic function is allowed to vary across countries, and for a given country to vary over time.

For the first quinquennium (1985-90), the rate of change (r,) is estimated from the rate of change in the rrevious uuinquennium (r0) and from the female secondary enrolment ratio (so) using the equations r, = .00379 + .723 ro - .000254 so for females, and r, = .01159 + .885 r0 - .000318 s. for males.

Percent urban is used in a few cases, with a different equation (see Bulatao and Bos 1989), where secondary enrolment is not available, and in fewer cases still the rate of change in the previous decade is used instead of the rate of change in the previous quinquennium when the latter appears to have been affected by exceptional circumstances. Limits are imposed on the rate of change for the first quinquennium (and for all other quinquennia), such that it cannot be greater than -.017 and less than _-.053._ For the second quinquennium, the rate of change is estimated as a function of the rate of change for the first quinquennium: r2 = -.007 + .8 r,.

The rate of change for the third quinquennium is estimated from the rate of change for the second quinquennium in the same way, but none of these projections influences estimates of current rates, which for most countries arc linear interpolations between projections of the first and second quinquennia rates. Table 2 shows how annual increments in life expectancy vary by initial levels of life expectancy. The minimum and maximum increments shown in the table result from the limits imposed on the rates of change.

The infant mortality rate is projected using a similar logistic function. The rate of change for each of the first three quinquennia is obtained from the equation rt = .0275 + .5 rt 1, _5_
# **Appendix Table 4. Effect of preschool availability on female’s work participation with preschool ages defined as 3-5**

||||**Work par**|**ticipation**|||
|---|---|---|---|---|---|---|
|Econometric strategy:|D|D|DD|D|DD|D-FE|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Public**|||||||
|Preschool * Eligible|||0.047***|0.047***|0.066***|0.063***|
||||(0.017)|(0.017)|(0.016)|(0.016)|
|Preschool density|-0.008|0.020|-0.010|0.007|-0.009|-0.002|
||(0.024)|(0.027)|(0.013)|(0.016)|(0.012)|(0.015)|
|Eligible child|||-0.046***|-0.047***|-0.031***|-0.031***|
||||(0.006)|(0.006)|(0.005)|(0.005)|
|**Panel B: Private**|||||||
|Preschool * Eligible|||0.001|0.001|0.002|0.002|
||||(0.002)|(0.002)|(0.001)|(0.001)|
|Preschool density|-0.007**|-0.004|-0.003*|-0.003*|-0.001|-0.004**|
||(0.003)|(0.003)|(0.002)|(0.002)|(0.002)|(0.002)|
|Eligible child|||<br>-0.041***|<br>-0.042***|<br>-0.029***|<br>-0.029***|
||||(0.010)|(0.010)|(0.008)|(0.008)|
|Observations|22,737|22,737|76,951|76,951|76,951|76,951|
|Mean|0.538|0.538|0.534|0.534|0.534|0.534|
|District Trend||X||X||X|

Note: Sample in column 1-2 is restricted to mothers with preschool-aged children (age 3-6). Sample in column 3-6 includes mothers with and without preschool-aged children (age 3-6). Sample is further restricted to PODES years. Column 1-2 regress work participation on preschool density in a difference-in-differences (DD) strategy (Section 4.1). Column 3-4 regress work participation on preschool density, a dummy for having a preschool-aged child, and the interaction between the two in a difference-in-difference-in-differences (DDD) strategy (Section 4.2). Column 5-6 builds on the DDD strategy and adds individual fixed effect (DDD-FE) to allow comparison within-mothers (Section 4.3). Column 2, 4, 6 add district-specific trends. All regressions include district, year, mother’s age fixed effects and an urban residence dummy. Panel A and B look at the effect of public and private preschool densities separately. Standard errors clustered at the district-level is shown in parentheses; *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively. Observations, work participation means, and inclusion of district trends are indicated in the last three rows.

72
# **3.2 Customs data**

Our export dataset is generated from raw data files collected by customs authorities containing export flows at the transaction level. The files were provided by the customs authorities of Malawi, Mali, Senegal and Tanzania. Each of them contains information on products exported at the highest level of disaggregation of the HS code used by these administrations: 10-digit for Mali and Senegal and 8-digit for Malawi and Tanzania. In addition to product information, each file contains information on destination market, FOB shipment value, net weight, port used and date of transaction. Original names and tax IDs identifying the individual firm were replaced by ‘dummy’ digital IDs so as to preserve confidentiality. We aggregated transactions up to annual totals at the 6-digit level, the standard level used in cross-country comparisons. Finally, for consistency, we filtered out years with different port coverage. For instance, for Malawi we have information from 2004 onward; however, as fewer ports were covered in 2004 than in other years, we excluded 2004 from our sample for that country. Sample periods are 2005-2008 for Malawi and Mali, 2000-2008 for Senegal, and 2003-2008 for Tanzania.

Table 1 presents some basic descriptive statistics. Tanzania has the largest number of exporters (1,359), followed by Malawi (856), Senegal (715), and Mali (280); however, they are less diversified than those of other countries in our sample in terms of markets. Mali’s exporters are, on average, the most diversified in terms of products.

Our variables of interests are indexed as follows. Let _f_ be a firm, _d_ a destination, _p_ a product (at HS6), _t_ the starting year of an export spell, and _vfpdt_<sup>_c_the dollar value of exports of product</sup><sup>_p_to destination</sup><sup>_d_in calendar year</sup> _t_ by firm _f_ from country _c_ . Because there are no multi-country firms in our sample, indexing observations by firm eliminates the need to index them by origin country. We aggregate transactions to annual ( _f, p, d, t_ ) quartets, our primary sample unit.

8
**Table 4 - Basic results for Development Outcomes  (Instrumental Variables - Log of Wheat Sugar Sustainability)** **<u>Human Opportunity Index</u>**

|Regression|Depende|nt Variable: Logaritm of Per Capita Income,Average 200|5-2010|
|---|---|---|---|
||IV|IV<br>IV ExcludingAmericas|IV|
|Human Opportunity Index*|0.035***|0.033**<br>0.040***|0.034***|
||(0.006)|(0.012)<br>(0.009)|(0.008)|
|WDI Gini Index - Average for 1960-2010||-0.008||
|||(0.021)||
|Americas - LAC plus Canada and United States|||0.465|
||||(0.302)|
|Europe and Central Asia|||0.357|
||||(0.259)|
|Middle East and Africa|||0.136|
||||(0.433)|
|constant|7.243***|7.696***<br>6.852***|6.997***|
||(0.425)|(1.477)<br>(0.610)|(0.621)|
|Number of Observations|49|46<br>39|49|
|F-statistics from first stage|23.43|20.58<br>14.51|7.87|
|**D-Index**||||
|Regression|Depende|nt Variable: Logaritm of Per Capita Income,Average 200|5-2010|
||IV|IV<br>IV ExcludingAmericas|IV|
|D-Index*|-0.097***|-0.086**<br>-0.122***|-0.091***|
||(0.019)|(0.034)<br>(0.028)|(0.023)|
|WDI Gini Index - Average for 1960-2010||-0.012||
|||0.019||
|Americas - LAC plus Canada and United States|||0.517|
||||(0.375)|
|Europe and Central Asia|||0.424|
||||(0.315)|
|Middle East and Africa|||0.013|
||||(0.504)|
|constant|10.590***|10.910***<br>10.770***|10.151***|
||(0.219)|0.423<br>(0.267)|(0.348)|
|Number of Observations|49|49<br>39|49|
|F-statistics from first stage|19.6|19.95<br>10.33|6.37|
|* p<0.05 ** p<0.01 *** p<0.001||||
|, ,<br>*Average Mathematics, Reading and Science Outco|mes Level 2 - PISA 2009|||
|**Gini Index**||||
|Regression|Depende|nt Variable: Logaritm of Per Capita Income,Average 200|5-2010|
||IV|IV<br>IV ExcludingAmericas|IV|
|Gini*|-0.157***|0.078<br>-0.246***|-0.295**|
||(0.030)|(0.071)<br>(0.044)|(0.099)|
|Human Opportunity Index*||0.061**||
|||(0.018)||
|Americas - LAC plus Canada and United States|||4.286**|
||||(1.335)|
|Europe and Central Asia|||-0.126|
||||(0746)|
|Middle East and Africa|||.<br>0.872|
||||(0.957)|
|constant|14.526***|2.732<br>17.418***|19.001***|
||(1.199)|(3.682)<br>(1.656)|(3.763)|
|Number of Observations|112|48<br>89|112|
|F-statistics from first stage|57.27|15.09<br>37.44|42.47|
|* p<0.05 ** p<0.01 *** p<0.001||||
|, ,<br>*Average 1960-2010||||

23