rafmacalaba/datause-encoder-data huggingface dataset:
85 ProJection (0009) with NRRa1 by 2045

# ETHIOPIA

# **Sunnary** for 25 Year Periods

|..**............**......................................<br>AGE**GROJP**|...<br>1985|2000|.<br> <br>2025|.................................................<br>2050|..................<br>2075|2100|2125|2150|
|---|---|---|---|---|---|---|---|---|
|...................................|||..............................................|...........................||||_|
|TOTAL M.F|42271|65919|123302|178949|219593|237512|242963|245343|
|MALES|||||||||
|0-4|3530|5999|8321|7697|7765|7832|7859|7866|
|5-9|3209|4740|7686|7501|7766|7843|7856|7855|
|10-14|2713|3994|7086|7802|7848|7853|7845|7843|
|15-19|2292|3117|6489|7995|7887|7817|7816|7826|
|20-2;|1922|3028|5933|7994|7762|7732|7779|.8O8|
|25-29|1602|2534|5351|7823|7529|7656|7761|7796|
|30-34|1333|2103|4373|7346|7328|7649|7762|7783|
|35-39|1103|1740|3631|6710|7579|7701|7751|7753|
|40-44|907|1435|2774|6063|7705|7697|7685|7697|
|45-49|736|1174|2634|5448|7611|7507|7548|7610|
|50-54|589|945|2133|4778|7282|7154|7361|7485|
|55-59|459|743|1677|3719|6560|6728|7135|7273|
|60-64|345|559|1270|2850|5584|6552|6804|6894|
|65-69|244|396|910|1916|4499|6022|6200|6251|
|70-74|154|251|594|1485|3361|5037|5177|5277|
|75+|125|198|soo|1380|3583|6730|7673|8195|
|TOTAL|21263|32955|<br>61363|88508|107649|11M512|<br>118011|119212|
|FEMALES|||||||||
|0-4|3526|5918|8197|7587|7635|7675|7690|7689|
|5-9|3097|4685|7594|7416|7660|7702|7698|7689|
|10-14|2618|3948|7008|7725|<br>7758|7725|<br>7695|7684|
|15-19|2212|3115|6424|7931|7819|7709|7678|7678|
|20-24|1868|2935|5893|7957|<br>7728|<br>7652|7659|7677|
|25-29|1569|2475|5345|7817|7524|7605|7665|7683|
|30-34|1311|2072|4390|7366|7348|7627|7690|7689|
|35-39|1091|1737|3666|6758|7629|7712|7707|7681|
|40-44|902|1450|2854|6140|7793|7751|<br>7679|7655|
|45-49|742|1202|2652|5567|7756|7622|7597|7616|
|50-54|606|986|2195|4963|7517|7356|7504|7580|
|55-59|483|793|1781|3965|6927|7074|7439|7528|
|60-64|373|619|1415|3162|6122|7150|7366|<br>7405|
|65-69|270|458|1070|2271|5207|6950|7109|7110|
|70-74|178|305|743|1825|4191|6290|6446|6523|
|75+|160|266|712|1988|5330|10398|12329|13243|
|TOTAL|21008|32964|61939|90440|111943|121999|124951|126132|
|BIRTH RATE||47.5|36.3|22.2|15.9|13.7|1U.0|12.8|
|DEATH RATE||17.4|11.6|7.6|7.8.|10.6|12.1|12.4|
|NET MIGRATI|ON RATE|-. 3|-.0|.0|.0|.0|.0|.0|
|GROWTH<br>RATE||2.96|2.50|1.49|.82|.31|.09|.04|
|TOTALFERTI|LITY|6.452|4.770|2.613|2.079|2.056|2.041|2.040|
|eCO) BOTH|SEXES|49.00|56.49|65.76|72.96|75.85|77.91|78.45|
|**IMR-** BOTH|SEXES|143.9|101.3|53.5|20.0|12.6|7.8|7.1|
# **2.4 Resources and Expenditures**

The average budget of EPAs surveyed is around 0.11 percent of the value of exports of goods and services, with a standard deviation of 0.35 and a median of 0.04 percent. The region with the largest average budget is Latin America and the Caribbean (LAC) at 0.17 percent of exports. It is followed by countries in Eastern Europe and Asia (EEA) at 0.12 percent, and then MENA, Sub-Saharan Africa (SSA), and the OECD with average budgets of around 0.09 to 0.10 percent of exports.

Regarding funding sources, around 52 percent of the agencies obtained more than 75 percent of their budget from public funding; 2 percent of the agencies obtained more than 75 percent of their budget from private funding; 3 percent of the agencies obtained more than 75 percent of their budget from selling their services (customer fees); and 2 percent of the agencies obtained more than 75 percent of their budget from either multilateral or bilateral donors. Thus, public funding seems to predominate as a source of funding. Three quarters of the agencies surveyed had no private funding, and half had no income associated with the selling of their services. When they reported some income, it represented on average less than 10 percent of their budget.

# **2.5 Activities and Client Orientation**

As mentioned, we considered four main activities: 1) country image building; 2) export support services; 3) marketing; and 4) market research and publications. The largest share of EPA budgets is generally spent on marketing and market research and publications. Another item which shows a large median –but also a much larger variance– is other activities not related to export promotion, except in the OECD, where the bulk (more than 75 percent of them) spent less than 10 percent on activities not related to export promotion. At the opposite end, in SSA other activities not related to export promotion represent between 10 and 25 percent of the budget of most agencies (at the median). The importance of export support services is also much larger in SSA than in other regions.

In terms of client orientation, the data cover the percentage of expenditures spent on large 6
Thanks to these data we can construct a relatively individualized measure of consumption, which is almost never available in household surveys. This is what we will use to assess individual economic welfare. The measure we use here is the amount of expenditures specific to the cell and not shared with any other cell plus the cell‘s imputed share of the household‘s joint expenditures. We will restrict our study to individuals who are heads of their cells. They are all adults with at least one dependent, and for consumption purposes, they are assumed to be the decision makers at the cell level. We are therefore left with 4401 observations, of which 56.8% are women. The average number of cells per household is 2.51. The range is from 1 to 12; 81% of the sampled households have more than one cell.

Importantly for the purpose of our paper, these data include information on parental characteristics and inheritance. If the parent has died, the survey asked whether he or she left any inheritance and then, for each person, whether they obtained any inheritance in the form of land, housing, money, durables or productive capital. No valuation of these inheritances was obtained. In particular, we do not know how much land was inherited, although the endogeneity concerns (which we return to) would clearly be even greater using amounts of inherited land rather than simply the incidence of inheritance.

# **4. Results**

We begin by describing inheritance patterns in our data, after which we study the associations with living standards, economic activity and mobility. (Appendix 1 gives summary statistics on the main variables used from the PSF.)

## **_Inheritance patterns_**

As can be seen in Table 1, many more fathers have any inheritance to leave to their heirs than do mothers. 72% of deceased fathers left land bequests, but only 22% of deceased mothers did so. Fathers bequeath their wealth to their sons more often than to their daughters, particularly when it is in the form of land. Mothers treated sons and daughters roughly equally in this respect.

With regard to parental characteristics, information was collected on the occupation and education of each parent for each individual. The last place of the mother and father‘s residence is also known (allowing us to know in particular whether an individual resides in the same village as his parents).

11
While there were just over 5,000 primary schools in Sierra Leone at the start of the program, the impact evaluation (IE) only focuses on schools that were registered with MEYS as having up to grade 6, the year when the National Primary School Examination is taken. The program included government, government assisted, and community schools, but not private schools. The IE focused on grades 4 and 5, as these are the standards by which education is principally in English, the language of the textbooks.<sup>2</sup>

# **3.2 Sampling**

The IE relies on a randomized experimental design using a two-step process. First, 4 out of 19 local councils, stratified at the regional level, were randomly selected. All 19 local councils were interested in participating in the study, thus one council from each of Sierra Leone’s four regions was chosen: Kailahun (Eastern region), Kambia (Northern region), Pujehun (Southern region), and Western Urban and Western Rural (who agreed to enter the randomization jointly). The locations of these councils are illustrated in Figure 1.

Within the randomly selected councils, 360 program schools were randomly selected using the EMIS data. From the universe of schools in these councils, schools that were already being targeted for textbook interventions by other organizations were excluded. Also, to avoid providing textbooks to schools that already had many, this project included only schools which had a 3:1 student-textbook ratio or higher. From the reduced sample, 90 schools were selected in each district (except for Western Rural and Western Urban which together amounted to 90 schools).

Within the local council, sample schools were assigned with equal probability to one of three treatment groups (30 schools in each group in each district): receiving textbooks, receiving textbooks plus teacher training, and a control group. Ultimately, due to multiple implementation challenges, the teacher training component was not undertaken. Therefore, for the IE, we are left with schools that received textbooks (treatment schools) and schools that did not (control schools).

Baseline data were captured on 341 schools (out of a total of 360). The 19 schools that were not included in the sample were mainly due to the inability of enumerators to access the schools due to either poor road conditions or flooded rivers. For a small portion of these schools, the enumerators were able to access the location, but there

> 2 Education at earlier grades is sometimes carried out in the Krio language.

6
- Hicks, J. H., M. Kremer, I. Mbiti, and E. Miguel (2013): “Vocational education in Kenya: Evidence from a randomized evaluation among youth,” Mimeo.

- Hirshleifer, S., D. McKenzie, R. Almeida, and C. Ridao-Cano (2016): “The Impact of Vocational Training for the Unemployed: Experimental Evidence from Turkey,” _Economic Journal_ , 126, 2115–2146.

- Honorati, M. (2015): “The Impact of Private Sector Internship and Training on Urban Youth in Kenya,” _World Bank Policy Research Working Paper No. 7404_ .

- “<sup> ́</sup>

- IBGE (2016): Indice Nacional de Pre ̧cos ao Consumidor Amplo (IPCA),” Instituto Brasileiro de Geografia e Estat ́ıstica, Sistema Nacional de<sup>`</sup> Indices de Pre ̧cos ao Consumidor (IBGE/SNIPC).

- Kluve, J. (2010): “The Effectiveness of European Active Labor Market Programs,” _Labour Economics_ , 17, 904–918.

- Kugler, A., M. Kugler, J. Saavedra, and L. O. H. Prada (2015): “Long-term direct and spillover effects of job training: Experimental evidence from Colombia (No. w21607),” _National Bureau of Economic Research Working Paper Series_ .

- N ̃opo, H., M. Robles, and J. Saavedra (2007): “Occupational Training to Reduce Gender Segregation: The Impacts of ProJoven,” _Inter-American Development Bank Working Paper_ .

- Reis, M. (2015): “Vocational Training and Labor Market Outcomes in Brazil,” _B.E. Journal of Economic Analysis and Policy_ , 15, 377–405.

- World Bank (2013): “World Development Report 2013: Jobs,” _World Bank Group. Washington, DC._

29
insufficient variation from Bowling (2014).

The terciles shown on the assessment results are based on assessment results from candidates assessed before the study started: 5,000 workseekers for communication, numeracy and concept formation test, and 500 workseekers for the other skills. Tercile assignments are largely unchanged if we retrospectively construct them using our full sample of assessed workseekekers.

Table A.2 shows the correlation of assessment results for the different skills. Numeracy, concept formation and communication have pairwise correlations of 0.34 to 0.52. Numeracy and communication assessments capture acquired knowledge, often from schooling, which is often positively correlated with fluid intelligence. This is potentially because learning at a higher rate improves acquisition of knowledge (Heckman and Kautz, 2012; Nisbett, 2009; Roberts et al., 2000). However, as we intended, these are less strong correlations between the other tasks (focus and planning) and the scales (grit, flexibility, and planning). These suggest the certificates will horizontally differentiate workseekers from one another.

# **B Implementation Costs**

This appendix reports the costs of the public certification intervention and compares these to gains experienced by treated workseekers, showing that the latter easily exceed the former. We measure costs from the Harambee and J-PAL Africa financial statements. All costs are reported in 2016/7 PPP USD terms and are averaged over the 2,247 candidates who received the public certification intervention. The cost figures in nominal USD are 42% of the cost figures in PPP USD, though this does not affect the cost-benefit comparisons. We report average variable costs and, where these are possible, total and average fixed costs. The average variable costs may change with scale but we do not attempt to project scale effects on costs.

The average variable cost of adding certification to Harambee’s existing assessment operation was USD 23.10. This included report printing, software license fees, website hosting fees, the time of J-PAL and Harambee staff used to prepare the reports, and the time of Harambee psychologists used to conduct briefings. This also included a USD 10.32 transport subsidy to each participant to cover the cost of travel to the Harambee office, which is arguably not a necessary cost of the intervention. These cost calculations exclude the private and placebo certifications, audit study, and firm-facing experiments.

The average variable cost of certification and assessment was USD 57.27 per participation. This included all certification-only costs, facility rental, computer rental, data and internet costs, and the time of Harambee staff who administered the assessments. Facility and computer rental costs were the largest line items for the assessment cost, jointly accounting for USD 23.43.

The average variable costs exclude fixed costs such as licenses for the assessment tools, market research into firm preferences over assessments, and senior management fees. For these costs we either cannot calculate a meaningful average fixed cost or cannot reliably separate Harambee’s 43
Against this backdrop, this paper seeks to determine the prevalence of price controls across EMDEs. To help answer the first question, this research contributes to the literature on price controls by presenting the findings from a new data set covering an almost complete set of EMDEs. This data set extracts the list of products subject to price controls from the latest available Trade Policy Reviews for each EMDE member country of the World Trade Organization. This list of products is compiled using existing legislation and additional material provided by country authorities. The data set provides a rough view of the prevalence of price control measures across countries. When combined with detailed information on country-level trade flows, the data set also provides an indication of imports and exports potentially subject to controls.<sup>2</sup> Armed with this data set, this study finds that while price controls are seldom imposed on goods in advanced economies, they are near ubiquitous in EMDEs and LICs in particular.

Second, this study seeks to enumerate the challenges that price controls impose for growth and development and government policies. While they may be introduced with the best intentions to improve social outcomes, available evidence suggests that price controls often undermine growth and development, impose fiscal burdens and can weaken the effectiveness of monetary policy. At least in part, this is because price controls cause a shift in consumption towards the subsidized good, and away from other non-subsidized goods. Moreover, when there are trend increases in international prices, or when they interact with barriers to entry, price control measures frequently morph into distortive subsidy regimes. Important social, fiscal and environmental costs are likely to follow, as well as adverse consequences for investment and employment, and productivity growth.

# **2. Use of price controls**

# **_Price controls on goods and services_**

Price controls are widely employed across advanced economies and EMDEs. They tend to be much more pervasive in EMDEs than in advanced economies, especially so for energy and food-related goods (Figure 1.A). The relatively high prevalence of other types of price controls in advanced economies reflects the greater prevalence of controls for services such as telecommunications. While price control regimes tend to be more restrictive in EMDEs when compared to advanced economies, the extent of controls varies greatly across economies (Figure 1.B).

> 2 Data on price controls on tradable goods combines the information on controlled prices from the World Trade Organization’s Trade Policies Reviews with 4-digit HS trade values from the World Bank’s World Integrated Trade Solutions database.

3
workers - the less skilled, the older and less able - are more adversely affected by GFC firm shocks. This finding suggests that the GFC through its induced adverse firm shocks plays a role in exacerbating long-run inequality.

# **IV. Mechanisms for Long-Run Effects**

## _A. Worker Reallocation across Firms and Sectors_

An important set of questions concerning the employment and wage losses from GFC firm shocks identified in Section III is whether they result from adjustments at the initial employing firm or whether worker movement to other firms or sectors counteracts such adjustments. Our rich worker panel database allows us to study the employment and wage reallocation margins for workers in response to their firm’s shock. We consider a set of mutually exclusive channels of adjustment for the workers, including change at the initial firm (for incumbent workers, i.e., those that remain employed by that firm), reallocation to other firms in the 2-digit sector of the initial firm, reallocation to firms in another tradable sector, and reallocation to firms in a non-tradable sector. This decomposition follows that proposed by Utar (2014) and captures both the direct impact of the shock on workers’ employment and wages at the initial firm but also subsequent worker movements that may offset such direct impact.

The results from this decomposition are presented in Table 3 for employment (panel A) and wages (panel B).<sup>33</sup> The negative and significant coefficient in column (2) in panel A - and its larger magnitude relative to that in column (1) - shows that the adjustment to GFC firm shocks is driven by incumbent workers that remain at the initial firm. Columns (3) and (4) in panel A show a weak countervailing effect of increased employment in other firms in the same sector or in another tradables sector.<sup>34</sup> Column (5) in panel A shows a significant countervailing effect of increased employment in other firms in non-tradable sectors but insufficient to offset the changes at the initial firm. The reallocation to other firms or sectors plays no countervailing effect for wages. This evidence suggests that our main finding of employment losses due to firm foreign shocks in Section III.B comes mostly from a new concept of _scarring for incumbent workers_ , not just displaced workers as in previous literature.

> 33Column (1) in each panel shows a baseline effect as that shown in Table 2 but estimated on a sample that does not include workers’ unemployment years. It is technically not feasible to use this decomposition for the filled-in worker panel database including observations when the worker is unemployed.

> 34For an incumbent worker who does not move from the initial firm, the values of the dependent variables employment or wages in other firms or other sectors in columns (3)-(5) are all 0.

16
Table 2: Returns to Education from OLS Estimates: Venezuela, 2000

|Variable|(1)|(2)|
|---|---|---|
|Years of Schooling|0.060<br>(19.6)|0.051<br>(15.6)|
|Age|0.065|0.068|
||(8.7)|(9.1)|
|Age<sup>2</sup>|-0.0007<br>(6.2)|-0.0008<br>(6.4)|
|Log hours worked|0.434<br>(15.5)|0.433<br>(15.6)|
|Years of education of head|-|0.013<br>(5.6)|
|Constant|8.535|8.474|
||(60.1)|(59.8)|
|R<sup>2</sup>Adj.|0.247|0.254|
|N|3,103|3,103|

Source: _Encuesta de Hogares por Muestro_ Note: t-values in parentheses The IV estimation results, using the education reform and parental instruments, are presented in Table 3. The sample consists of male children of the head of household. In columns (1) and (2) the reform instrument is used alone; in column (2), father’s years of education is an additional regressor. In column (3) the only instrument used is father’s years of schooling completed. In the next few paragraphs, before discussing the IV estimation results, we present the framework for interpretation of the results.

Theory suggests that schooling choices are determined from a cost-benefit calculation, given alternative choices (see Angrist and Krueger 2001). Assuming heterogeneity in ability and schooling costs (for a discussion, see Card 2001; Ichino and Winter-Ebmer 1998; Angrist, Imbens and Rubin 1996; Imbens and Angrist 1994), consider the education reform of 1980 as instrument (labeling it _Zi_ ) which affects a subset of individuals in the sample. Denoting individual _i_ ’s marginal return to schooling by _βi_ , the probability limit of the IV estimator using instrument _Zi_ is: Plim βZi = Cov[log yi, Zi] / Cov[Si, Zi] 13
- Hanson, Gordon. 2004. ―What Has Happened to Wages in Mexico since NAFTA?‖ in Toni Estevadeordal, Dani Rodrik, Alan Taylor, Andres Velasco, eds., _FTAA and Beyond: Prospects for Integration in the Americas_ (Cambridge: Harvard University Press).

- Hanson, Gordon H. 2006. ―Emigration, Labor Supply, and Earnings in Mexico,‖ in _Mexican Immigration_ , George J. Borjas, ed. (Chicago: University of Chicago Press.)

- Hanson, Gordon H., Raymond Robertson and Antonio Spilimbergo. 2002. ―Does Border Enforcement Protect U.S. Workers From Illegal Immigration?‖ _Review of Economics and Statistics_ , Vol. 84, No. 1: pp. 73-92.

- International Organization for Migration. 2007. _Migration as it is_ (Chisinau: International Organization for Migration).

- Kifle, Temesgen. 2009. ―The Effect of Immigration on the Earnings of Native-Born Workers: Evidence from Australia,‖ _Journal of Socio-Economics_ , Vol. 38, No. 2: pp. 350-56.

- Lucas, Robert E. B. 1987. ―Emigration to South Africa's Mines,‖ _American Economic Review_ , Vol. 77, No. 3: pp. 313-30.

- Lucas, Robert E. B. 2005. _International Migration and Economic Development: Lessons from Low-Income Countries_ (Cheltenham, UK and Northampton, MA: Edward Elgar Press).

- Mishra, Praci. 2007. ―Emigration and wages in source countries: evidence from Mexico,‖ _Journal of Development Economics_ Vol. 82, No. 1: pp. 180–199.

- Munshi, Kaivan. 2003. ―Networks in the Modern Economy: Mexican Migrants in the US Labor Market,‖ _Quarterly Journal of Economics_ , Vol. 118 (May): pp. 549-97.

- Mwabu, Germano and T. Paul Schultz. 1996. ―Education Returns Across Quantiles of the Wage Function: Alternative Explanations for Returns to Education by Race in South Africa,‖ _American Economic Review_ , Vol. 86, No. 2: 335-339.

- Orrenius, Pia and Madeline Zavodny. 2007. ―Does Immigration Affect Wages? A Look at Occupation-Level Evidence,‖ _Labour Economics_ Vol. 14 (October): pp. 757773.

- Robertson, Raymond. 2004. ―Relative Prices and Wage Inequality: Evidence from Mexico,‖ _Journal of International Economics_ Vol. 64, No. 2 (December): pp. 387-409.

17