rafmacalaba/datause-encoder-data huggingface dataset:
# **C Mother Tongue Instruction**

## **C.1 Background**

Ethiopia has more than 80 languages (IPUMS, 2017), and in 1991, for the first time in more than a generation, the transitional government introduced classroom instruction in languages other than Amarigna. Amarigna was the language preferred by the government throughout the previous decades; however, by the end of the Transitional Government’s second year in power, the mother tongue language for 80 percent of the country’s residents had been introduced as a language of instruction (Boothe and Walker, 1997; Zenebe Gebre, 2014). The introduction of the mother tongue instruction program was complex.<sup>21</sup> In 1991, the Transitional Government’s Council of Representatives selected the first four languages to be introduced in the mother tongue instruction program. These languages were selected from a list of 14 that were originally used in a 1979 adult literacy campaign (Boothe and Walker, 1997). The most common language selected was Oromigna, it was spoken by 31 percent of the population at the time of the 1994 population census, roughly the same portion of the population that spoke Amarigna. The other three languages selected for the initial wave of the program were Tigrigna (6%), Sidamigna (3.5%), and Wolayitigna (2.3%). These languages were respectively the fourth, fifth and sixth most common mother tongues in the country at the time. The selection of what was almost precisely the largest languages reduces concern of favoritism from the central government, and because the new regional boundaries were drawn along traditional ethnolinguistic borders, there was little variability of where the languages could be introduced. The following round of languages were introduced in 1993, this group comprised of Somaligna (6%) and three languages within the diverse Southern Nations, Nationalities, and Peoples’ Region (SNNPR) totaling an additional 4 percent of the population (Boothe and Walker, 1997; Zenebe Gebre, 2014).<sup>22</sup> At this point every language spoken by at least two percent of the population had been introduced, and as time passed political calculations are likely to have a role in the selection process for less prominent languages. Therefore, this paper focuses on the implementation of the initial phase of the MTI project.

The final key aspect of the introduction of the MTI program was the need to translate the primary school material from Amarigna into the new languages. Up to this point in time, any written translations of these languages had used the Ge’ez script; however, due to the historical connotations of the use of Amarigna, each region was given the option to use a different script in translation, and every region outside of Tigray selected to translate their schooling material into the Roman script. For Tigray, this allowed them to translate all of the material locally and introduce the MTI program on schedule in 1991 (Boothe and Walker, 1997;

> 21For consistency, each language will be referenced using the name from the 2007 Ethiopian Census.

> 22The languages were Hadiyigna, Gedeogna, and Kembatigna.

A.11
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34
## **FIGURE 2. SELECTED PUBLIC EXPENDITURE AND FINANCIAL ACCOUNTABILITY (PEFA) INDICATORS BY COUNTRY, 2005-07**

<!-- Start of picture text --> Bangladesh Dominican Republic Macedonia Mozambique Ukraine<br>Aggregate expenditure outturn compared to original approved budget<br>Effectiveness of payroll controls<br>Availability of information on resources received by service delivery unit<br>4<br>3<br>2<br>1<br>Selected PEFA budget performance indicators 0<br><!-- End of picture text --> Source: PEFA Secretariat (various years).

# **Budget Leakages**

Weaknesses in budget management can result in funds never arriving at their intended destination due to mismanagement or theft. And, where allocated health funding is not disbursed, or is diverted en route from the point of disbursement to health facilities, health service delivery tends to suffer.

Public Expenditure Tracking Surveys (PETS) and Public Expenditure Reviews (PERs) are means to track funds and scrutinize the flow of public resources in health across layers of the administrative hierarchy (Engberg-Pedersen et al. 2005; Reinikka and Smith 2004; Savedoff 2008).<sup>9</sup> Tracking inflows, understanding spending, and identifying where governance failures may arise, PETS and PERs complement what PEFA evidence reveals about government-wide performance, thereby offering an important diagnostic on budget management and possible leakages.

Given the complexity of health systems PETS can be a useful tool for determining the stage at which leakages occur in the flow of funds, and what types of resource flows are most vulnerable to leakage in a particular country. There are several potential leakage

> 9 For access to available PETS by country see http://go.worldbank.org/HSQUS4IS20. Also see Savedoff (2008).

17
The above analysis is based on the cyclically-adjusted primary deficit estimates described earlier. To assess its robustness, we can examine an alternative measure of the change in the fiscal stance, due to Blanchard (1993), which avoids taking a stand on the nature of business fluctuations or on trend-cycle decomposition techniques. It compares actual revenue and expenditure with those that would have happened if the previous period's "economic environment" (as described by unemployment and trend output) had prevailed. Such calculation is reported in Figure 4.6. The qualitative pattern is very similar to that shown earlier in Figure 4.5: a major expansionary change in fiscal policy from mid 1996 to 1998, reversed after that date (with brief interruptions in 2000 and 2001).

**Table 4.2. Interest Payments on Public Debt**

||**Interest**<br>**Payments**<br>**onDebt**|**Change in**<br>**Interest**<br>**Burden**|**Contrib**<br>**Cha**<br>**Interest**|**utionto**<br>**ngein**<br> **Burden**|
|---|---|---|---|---|
||Percentof<br>GDP|Percentof<br>GDP|Debt<br>Volume|Interest<br>Rate|
||||Effect|Effect|
|1991|2.8||||
|1992|1.6|-1.1|-0.4|-0.8|
|**1993**|1.4|-0.2|0.1|-0.3|
|1994|1.6|0.1|0.1|0.0|
|**1995**|1.9|0.3|0.2|0.1|
|1996|2.1|0.2|0.1|0.1|
|1997|2.3|0.3|0.1|0.2|
|**1998**|2.6|0.3|0.2|0.1|
|**1999**|3.4|0.8|0.5|0.3|
|**2000**|4.1|0.7|0.3|0.4|
|**2001**|5.4|1.3|1.0|0.3|
|**TOTAL**|||||
|**1991-2001**||2.6|2.2|0.5|
|**1993-2001**||3.9|2.4|1.5|

**Source:** Ministerio de Economia de **la** Republica Argentina and Intemational Monetary Fund.

However, the adjustment in the structural primary balance after 1998 was not enough to compensate for growing interest rate payments. Figure 4.7 shows how the Federal government's structural overall balance, calculated as the structural primary balance plus actual interest payments, was kept in negative territory (around 1% of GDP) since the end of 1996. Indeed, interest payments rose from around 2% of GDP in 1995/96 34
Kenya.

There is a growing body of literature using quantitative spatial general equilibrium models to assess the impacts of infrastructure. Allen and Arkolakis (2014) develop a general equilibrium framework to determine the spatial distribution of economic activity and uses to assess the impact of the US interstate highway system. Michaels et al. (2011) look at changes in sectoral employment as an outcome that captures the underlying infrastructure-induced effects.<sup>2</sup> Bustos et al. (2016) and Fried and Lagakos (2020) study the general equilibrium implications of electrification via its effect on productivity. Several papers provide policy counterfactuals for future road and border infrastructure improvements for the Belt and Road Initiative (Lall and Lebrand, 2020; Bird et al., 2020); in Bangladesh (Herrera Dappe and Lebrand, 2019); and between Bangladesh and India (Herrera Dappe et al., 2021). The paper contributes to this strand of the literature by assessing the spatial general equilibrium impacts of several planned transport investments and trade facilitation measures in neighboring countries that are at different stages of development.

The paper is structured as follows. Section 2 provides an overview of the background in the Horn of Africa. Section 3 includes details of the data used in our analysis. Section 4 presents the empirical strategy and results. Section 5 develops a spatial general-equilibrium model to produce counterfactuals for more domestic and regional integration. Section 6 concludes.

# **2 Background**

Economic growth in the Horn had been relatively strong before the COVID-19 pandemic struck, but the subregion still lags behind other African countries and the pace of structural change has been slower than expected. At approximately $1,000, the per capita income in the Horn of Africa remains well below the Sub-Saharan African average of about $1,500. Structural transformation out of agriculture is at different stages across countries. Employment in nonagricultural sectors accounts for about 45 percent of total employment in Kenya, 35 percent in Ethiopia, and less than 20 percent in Somalia (Figure 1). The share of employment in agriculture in Djibouti, Ethiopia, and Kenya has been declining since the mid-2000s. In contrast, it has stagnated at high levels in Somalia. The main challenge for the subregion is generating enough adequate-quality jobs through economic transformation.

2See Redding and Turner (2015) and Redding (2016) for surveys of the literature on the impacts of transport infrastructure and the growing use of models to quantify these impacts.

4
and some extensions, and explores directly the empirical consequences of using FAI rather than FDI data. Section 6 applies the results to assess the implications for real inward investment of the prospective global corporate minimum tax rate. Section 7 concludes.

# **2 Channels of Tax Spillover**

To fix ideas, consider a multinational potentially investing through controlled affiliates in _N_ countries. Suppose too, for now, that profit shifting through artificial transactions between them is not possible: relaxing this will be one of the later extensions. Denoting by _Kj ≥_ 0 the tangible capital that the multinational invests in its affiliate in jurisdiction _j_ , and by _Fj_ ( _Kj_ ) some output associated with that capital, we suppose taxable receipts of the affiliate in the typical host jurisdiction _h_ to be of the form _Rh_ [ _F_ 1( _K_ 1) _, .., Fh_ ( _Kh_ ) _, .., FN_ ( _KN_ )].<sup>11</sup> The key feature this allows for is the possibility that receipts of an affiliate located in any country _h_ may depend on the production of related affiliates in other countries. This serves to capture a range of possible patterns of multinational activity.

The simplest such possibility is that each affiliate serves its own local and competitive market, selling its product at price _Ph_ , so that _Rh_ = _Ph.Fh_ ( _Kh_ ); this is in effect the familiar closed economy case, with no cross effects across the distinct affiliates. But there are many other possibilities captured by this structure. One, a case of particular interest in analyzing the consequences of closer economic integration, is that the affiliates sell a homogeneous product into a single integrated market within which the multinational has some degree of market power, so that _Rh_ = _P_ (<sup>�</sup><sup>_N_</sup> _i_ =1<sup>_Fi_)</sup><sup>_Fh_,where</sup><sup>_P_(</sup><sup>_._)denotesinversedemand,with</sup> _P_<sup>_′_</sup> _<_ 0.<sup>12</sup> In this case, each cross effect _∂Rj/∂Fh − P_<sup>_′_</sup> _Fj_ is strictly negative: increased output in _h_ reduces the price at which all affiliates sell, and so reduces revenue in all countries other than _h_ itself. Another possibility is that expanding output in affiliate _h_ diverts scarce managerial resources away from other affiliates, again implying a negative cross-effect. There may also be circumstances, however, in which cross effects are positive, reflecting some

> 11The _Fj_ ( _Kj_ ) are assumed increasing and strictly concave; intermediate goods supplied by third parties is taken to be subsumed in the revenue functions.

> 12Derivatives are indicated by a prime for a function of one variable.

6
|**France-**<br>**Institut**<br>**Nationale de**<br>**la statistique**<br>**et des etudes**<br>**economiques**<br>**(INSEE)**|Individuals who worked for any amount of time, even if<br>only for an hour in the course of the reference week. Only<br>individuals in the working-age population (between 15 and<br>64 years of age) are considered.|All people aged 15 and older who do<br>not have a job and are looking for one.|https://www.i<br>nsee.fr/en/met<br>adonnees/defi<br>nitions|
|---|---|---|---|
|**International**<br>**Labor**<br>**Organization**<br>**(ILO)**|All those of working age (15 years and over) who, during a<br>short reference period, were engaged in any activity to<br>produce goods or provide services for pay or profit. They<br>comprise employed persons "at work", that is, who worked<br>in a job for at least one hour; and employed persons "not at<br>work" due to temporary absence from a job, or to working-<br>time arrangements (such as shift work, flextime and<br>compensatory leave for overtime).|All those of working age (15 years and<br>over) who were not in employment,<br>carried out activities to seek<br>employment during a specified recent<br>period, and were available to take up<br>employment given a job opportunity.|https://www.il<br>o.org/ilostat-<br>files/Docume<br>nts/Statistical<br>%20Glossary.<br>pdf|

The definition of official employment and unemployment rates in MENA appear to be inconsistently reported across countries. Many MENA countries do not comply with the ILO’s definition of employment or unemployment—or do not provide sufficient information to indicate whether the definitions are followed (see Table 5). Oman, which has one of the highest ODIN scores in the region, does not specify the definition of employment and unemployment. Countries in conflict, such as the Syrian Arab Republic and Yemen, do not provide any information on unemployment. Changes in definitions may entail shifts in the employment and unemployment numbers reported (Arezki et al., 2020).

**Table 5 Definitions of Employment and Unemployment**

||Follow ILO<br>unemployment definition|Follow ILO employment<br>definition|Age of working<br>population|
|---|---|---|---|
|Morocco|YES|unspecified|15 and above|
|Algeria|YES|YES|unspecified|
|Tunisia|YES|unspecified|unspecified|
|Libya|YES|unspecified|15 and above|
|Egypt, Arab Rep.|YES|YES|15 and above|
|Lebanon|YES|unspecified|YES|
|West Bank and Gaza|YES|unspecified|15 and above|
|Jordan|YES|unspecified|15 and above|
|Saudi Arabia|NO|unspecified|15 and above|
|Oman|Unspecified|unspecified|unspecified|
|United Arab Emirates|YES|unspecified|YES|
|Qatar|YES|unspecified|15 and above|
|Bahrain|YES|YES|unspecified|
|Kuwait|YES|unspecified|YES|
|Iran, Islamic Rep.|YES|YES|NO|
|Djibouti|NO|NO|unspecified|
|Iraq|unspecified|unspecified|unspecified|

17
given their welfare status in the other period (i.e., conditional probabilities). The former measure provides one way to define chronic poverty,<sup>6</sup> and the latter permits the study of poverty transitions. We also provide the formulae for the standard errors on point estimates, which is not available with the bound estimates offered by Dang et al (2014).

Second, we generalize the construction of these synthetic panels to settings where more than two rounds of data are available. This offers useful results since few actual panel datasets in developing countries span more than two periods; and in those cases where they do, they may be suffereing heavily from attrition problems. By considering poverty mobility in more than two periods, we can investigate richer inter-temporal profiles of movement into and out of poverty.

Third, our proposed framework further extends Dang et al. (2014)’s investigation of household transitions into and out of poverty to a much more general setup of household movements among different consumption groups. A typical description of the former is usually shown in a 2x2 poverty transition matrix—where household movement is tracked between a poor category or non-poor category from one period to the next—while an example of the latter is a 5x5 consumption transition matrix, where household movement is tracked not just for two poverty categories but for five consumption quintiles.

We validate our estimates both theoretically with a Monte Carlo simulation exercise and empirically by using cross sectional and actual panel survey data from several high-income and developing countries including Bosnia-Herzegovina, Lao PDR, Peru, the United States, and Vietnam. We find that our synthetic panel estimates are close to—and mostly lying within

> 6 Also see Calvo and Dercon (2009) and Foster (2009) for more discussion on different definitions of chronic poverty. We restrict our discussion in this paper to a money-metric measure of poverty, for a multidimensional measure see Alkire and Foster (2011).

5
et al., 2018). The year 2014 also saw the passing of carbon tax legislation with the _General Tax Reform Bill_ .<sup>8</sup>

# **4 Data**

The Chilean _Annual National Industrial Survey_ (ENIA from now on) is made available by the _National Statistical Institute_ (INE, 2015). ENIA data is publicly available and spans the period 1995-2019. From 1995 to 2007, ENIA offers a harmonized panel data set (referred to as the _combined_ data) which identifies the same manufacturing firm with a unique ID linking plants over time. At the time of this study, unique firm IDs linking panel data were unavailable and, until recently, ENIA had microdata available from 2016-2019 in separate annual waves (without a unique firm ID over time). We refer to these separate waves as the _raw_ version on ENIA.<sup>9</sup> The combined ENIA data set suffers from a lack of regional and manufacturing sector-level identifiers. To recover information on the location and the ISIC 4-digit codes for each plant, we resort to the raw data version of ENIA, which contains both desired variables. We match plants in the combined and raw data by their common characteristics and apply the same matching techniques to expand the panel structure in the post-2007 period. This procedure yields a final data set containing 100,803 observations on 12,169 plants, with an average of 4,200 unique firms observed across survey years. We provide a more extensive discussion on the matching algorithm in Appendix C. As illustrated in Figure A.2, the matching algorithm achieves a stable firm sample over the entire sample horizon and a low attrition rate.

The combined data also exhibit irregularities potentially stemming from misreported electricity and fossil fuel profiles. We address this issue by employing a simple cleaning algorithm, which provides a rule-based procedure to identify and correct inter-temporal changes in reported quantities and unit prices of pre-defined magnitudes. We provide a more extensive discussion on the cleaning algorithm in Appendix D.

> 8With its inception in 2014, the tax reform (Ley 20.780) falls too close to the end of the sample period to merit an empirical analysis. At the time of writing, the implementation of the policy remains an ongoing process; see the World Bank’s Results Brief 15 February 2021 and project Market Instruments for Climate Change Mitigation in Chile (last access: September 2022).

> 9The authors are working on a revision of the results using the newly available data.

7
leaving for self-employment. The probability of finding ajob, p x = 0.2148, is calculated using the formula p* = **_h_** _*_ with a hiring (quitting) rate h* = q = 0.02448, a formal-sector employment level I * = 0.637, and a fraction of self-employed workers searching for a formalsector job,v (1 - q *), taken to be 0.2 (see the discussion in Section 3 for the value 0.2). The implied value of v is 0.21. Assigning a value to 13 is complicated by the assumption that workers do not participate in capital markets, which implies that we cannot simply follow the real business cycle literature and infer the discount factor from the long-run real interest rate (rate of return to capital). Of course, if workers' discount factor were equal to the discount factor of capitalists, then this procedure would still be valid. But one reason why workers have only a small amount of wealth might be exactly their impatience relative to capitalists. We decide to use a discount factor 13 = 0.9 for the baseline model, which is considerably lower than the 0.96 used in the real business cycle literature.<sup>2 0</sup> Table 2 summarizes the choice of parameter values for the baseline model.

# _b) Simulation_

Consider first a tax reduction program which lowers the tax on formal-sector labor income, r, by 1% but leaves the (after-tax) earnings of self-employed workers, b, unchanged. For simplicity, we also assume that any change in government revenues is met by a corresponding change in government spending so that the fiscal budget is balanced before and after the tax reform. As shown in table 4, the quantitative effects of such a tax reform on all three variables of main interest is substantial: the before-tax wage increases by 0.68%, the labor-turnover rate decreases by 2.63% (of its initial value of .02448), and the formal-sector employment level rises by 0.91% (of its original level of 0.637).

Figure 1 shows the effect of the tax reduction program as shifts in the labor demand and efficiency wage curve. The labor demand curve shifts to the right: for given before-tax wage, w, 2 00f course, the value of 0.96 is derived by considering US data.

21