rafmacalaba/datause-encoder-data huggingface dataset:
significant tax revenue due to the negative impact of power outages on firms’ revenue and profits. Therefore, reducing or eliminating outages by fixing the problems of the power sector in the respective countries will, other things being equal, lead to tax-revenue savings. We hypothesize two potential channels through which outages affect tax payments from the industrial sector: intensive and extensive margins. On the intensive margin, outages reduce the profitability of existing firms, thereby reducing tax payments from these firms. On the extensive margin, electricity-supply irregularities constitute a distortion in the business environment and can constrain the establishment of new firms and hence stifle expansion of the tax net.

Thus the total tax revenue losses/gains for each country _j_ is estimated using the expression in equation (4).

where _τj_ and _θj_ represent, respectively, , the share of total tax payment(s) of existing firms lost due to the adverse effects of electricity shortages on the operations of existing firms and the percentage increase in the tax base resulting from new businesses created in response to the provision of reliable electricity, while _δ_<sup>_t_</sup> and _t_ represent discount factor and years, respectively. _CITRev_<sup>_∗_</sup> is the (potential) corporate income-tax revenue accruing to the state in the absence of power outages. Details on the steps and assumptions underlying the simulations of tax-revenue losses/gains are provided in the Appendix.

# **3.2 Data**

The analysis relies heavily on data from the Afrobarometer and Enterprise Surveys data sets, complemented with data from secondary sources, including the Firm Informality Survey, Mo Ibrahim Index of African Governance, Worldwide Government Revenue Database, and World Development Indicators. Summary statistics of the data used in the paper are presented in Tables 1 and 2.

## **3.2.1 The Afrobarometer data**

Afrobarometer is a research network that conducts surveys of public attitudes on democracy, governance, economic conditions, and related issues in African countries. The series currently contains data from 37 African countries collected during the period 1999-2015. Data from Round 6 (2014/2015) of the Afrobarometer series are used for the analysis. The 11
of a field experiment. Users received Mirt stoves under three randomly assigned price schemes and two networking options that are discussed in the following section. We then examine the extent of stove use over the course of more than one year.

# **3. Sampling and Experimental Design**

# _3.1 Sample site selection_

The sample sites are chosen from that used in a 2012 study by the Environmental Economics Policy Forum for Ethiopia (EEPFE) based at the Ethiopian Development Research Institute (EDRI) that identified 110 sites from which forestry and related data were collected. It was the community level information collected from these study sites that was used to assign treatments and test randomization.

Out of the 110 sites, we remove 15 sites that were covered during a pilot survey conducted to inform our research. We also remove all sites from Tigrai Regional State as open three-stone traditional stoves, which are used throughout most of the rest of Ethiopia, are not used in this region. We also remove three sites from Borena District, because _injera_ is not typically eaten. From the 81 remaining sites we select 36 sites at random using proportionate random sampling based on regional state forest cover. Forest cover was used, because most fuelwood in the sample areas come from forests. Forested area also proxies well for total regional state population and area. Based on this criterion 20% of the observations come from Amhara region, 50% from Oromiya region and 30% from SNNP. The selection of sites within each regional state was done using simple random sampling.

A site in this study is a village (locally called _Got_ or sub- _Kebele_ ). A list of households for each site was obtained from the local administration. All sites have formal or informal forest user groups. A total of 504 households were selected from the 36 villages (14 households from each site) using systematic random sampling. A total of 360 households (10 households from each site) received the improved Mirt stove and the rest were control households who did not receive the Mirt stove. In this paper, which deals with Mirt stove use frequency, we do not use results from the controls.

**8 |** P a g e
**Table 4. Social accountability training balance tests**

||(1)<br>Project Funds|(2)<br>Livestock<br>project|(3)<br>Project<br>start date|(4)<br>High<br>reported<br>corruption|(5)<br>Male|(6)<br>Can write|(7)<br>Can read|(8)<br>Distance to<br>s/c<br>headquarter|
|---|---|---|---|---|---|---|---|---|
|Training|152,054|0.002|-0.030|0.018|0.050***|0.051***|0.036**|0.760|
||[458,459]|[0.026]|[0.314]|[0.035]|[0.015]|[0.016]|[0.016]|[9.737]|
|Control means|20,700,000|0.729|38.38|0.33|0.425|0.563|0.563|81.642|
|N|895|895|812|846|5,915|5,913|5,914|5,840|
|R-squared|0.977|0.624|0.346|1.000|0.088|0.183|0.156|0.072|

Notes: This table reports a balance test for project and individual-level characteristics. Columns 1 to 4 are measured at the project level during the project assessment. Columns 5 to 8 are from the endline household survey and represent participant characteristics that were not expected to change due to the treatment. Standard errors are reported in brackets below the coefficients. *** p< 0.01, ** p< 0.05, *p< 0.1.

40
The scoring for Kenya is described below. It is based on the results of the World Bank Regulatory Survey in East Africa<sup>42</sup> and the World Bank Survey on Applied Policies in Services<sup>43</sup> .

# **Barriers to establishment Form of establishment Score 0.5**

Foreign service providers are required to incorporate or establish the businesses locally. There are no restrictions on forms of incorporation. **Foreign partnership/joint venture/association Score 0** No restrictions. **Investment and ownership by foreign professionals Score 0** No restrictions. **Investment and ownership by non-professional investors Score 0.5** An engineering/ consulting firm must have at least one Partner/Director registered as Consulting Engineer who has in force an Annual Practicing Licence in the specified disciplines. **Nationality/citizenship requirements Score 0** No restrictions. **Residency and local presence Score 0** No restrictions. **Quotas/economic tests on the number of foreign professionals and firms Score 1** Entry permits are issued to non-citizens with skills not available at present in the Kenya (class A entry permits for management and technical staff - horizontal measure in Immigration Act Cap 172).

# **Licensing and accreditation of domestic professionals Score 1**

Membership in association is compulsory. Professional examination, practical experience and proof of higher education are required. **Licensing and accreditation of foreign professionals Score 0.75** Foreign professionals must be registered members of the Engineers Association. Foreign professionals must be holder of a diploma, degree or other qualification recognized by the Association of Engineers of Kenya.

**Movement of people - permanent Score 0.5**

There are limits on the duration of stay; in general, duration of stay is determined on a case by case basis. **On-going operations Activities reserved by law to the profession Score 1** 42 The regulatory surveys were conducted by local consultants who interviewed the professional associations in the examined East African countries in 2009. 43 The policy surveys were conducted by DECRG in 2008-2009.

110
_5_ developed countries as markets for African exports and, within the developed country group, the singular importance of the EEC.

For Sub-Saharan Africa as a whole about 78 percent of all exports go to developed countries and this share equals 90 percent or more for Liberia, Mauritius and Nigeria. Roughly 63 percent of all African exports to developed countries are destined for the EEC (these shipments account for 47 percent of African exports). North America accounts for about onehalf the value of EEC trade. Japan is marginal, receiving less than 3 percent of African exports. One-half (14 of 28) of the African countries have over 50 percent of their exports destined for the EEC -- the share exceeds 75 percent in the case of Liberia, Mauritius and Reunion. In short, Table 1 clearly indicates that Uruguay Round trade liberalization measures adopted by the EEC will be of key importance for African countries, but developments in the North American market will also have a major impact.

Having established the importance of OECD markets (particularly the EEC) for Africa, a second related consideration for assessing the influence of MFN tariff cuts concerns identification of the major African export products. Table 2 provides an indication by tabulating the value and share of the 30 largest non-oil three-digit SITC products exported by Afiican countries to OECD markets in 1970, 1980, and 1990.5 These export items account for approximately $16.5 billion in trade or 92 percent of total non-oil African exports to OECD markets. In addition, the table also provides s:milar information for five broad classes of 5Several earlier years of data have been included to help assess changes in the relative importance of African exports. Crude petroleum (SITC 331) dominates African trade, with this single three-digit product accounting for over 50 percent of total exports. Petroleum was excluded from Table 2 in order to better indicate the relative importance of other products to African countries that are not energy exporters. Also, crude petroleum generally does not face OECD trade barriers so it would not be directly affected by the Uruguay Round.
exports were shipped by the end of February. Being financially handcuffed by the banks and eventually obtaining a lower quality stock of supplies, it is likely that the average export prices earned by the TCMB will have been considerably lower than those obtained by the private traders.

As noted earlier, private trader efforts during the 1991/92 season to export were largely unsuccessful, with both product quality and buyer unreliability problems. Firms entering the trade during the 1992/93 season faired much better. FOB prices were mostly within a range of $675 to 715/Ton. While lower than for the previous season (when average exports were $813/Ton), the exporters benefitted from a devaluation of the Tanzania Shilling from 290 per $1 in March 1992 to 345 per $1 a year later. This, together with the lower producer prices, provided comfortable trading margins (e.g. 12 - 20%) for most exporters this season. The trading costs and earnings for two companies (two transactions) are illustrated in Table 19.

|Table 19: Illustrati<br>l________________________<br>|ve Exporter Trading Costs and P<br>(Figures in Tsh./Kg. or $/Ton)<br>|rofits (1992/93 Season)<br> <br>l<br>|
|---|---|---|
|Cost or Price Item|Cashew Nuts Procured in<br>Masasi in January and<br>Exported via Mtwara Port in<br>Late February|Cashew Nuts Procured in<br>Tinduru in December and<br>Exported via Mtwara Port in<br>Late January|
|Price to Producer|127|130|
|Levies/Cesses|18.0|15.0|
|Commissions to Buying<br>Agents and/or Cooperative<br>Officials|6.0|_6.5_<br>I|
|Transport Costs to Mtwara|16.0|19.0|
|Packaging Materials|4.35|4.35|
|Weight Loss|1.4|1.5|
|Finance Charges|13.25|13.56|
|Overhead Costs|4.0|4.5|
|Port/Inspection/Handling|6.6|6.6|
|Total Costs|196.6<br>at Tsh. 345=$1<br>$570/Ton|201.01<br>at Tsh. 330.3=$1<br>$608.8/Ton|
|F.O.B. Earnings'|$689.5/Ton|$691.5/Ton|
|Exporter Margin|$119.5/Ton (20.9%)|$82.7/Ton (13.6%)|

1 Assuming 1.5% weight loss in transit and $68/Ton for freight and insurance.

Source: Author's Field Interviews 38
in enrollment rates between urban and rural youth in developed countries. In developing countries, there is a large 15 percentage point gap in enrollment rates favoring urban youth.

As young people start leaving school, some join the labor force. An individual is considered part of the labor force if he is either employed or if he is not employed but is actively seeking work. The labor force participation rate is the broadest measure of labor supply—a willingness to provide time to the labor market.

Figure 2 shows that the average youth labor force participation rates are very similar between developed and developing countries. However, adult labor force participation rates are higher in developed than developing countries. There is no difference in female youth labor force participation rates between developed and developing countries. Male youth participate in the labor market with greater frequency in developing countries.

In developing countries, youth are slightly more engaged in the labor market than are youth in developed countries. The gap between developing and developed country youth LFPR is five percentage points for rural youth and only two percentage points for urban youth.. In both sets of countries, male youth have higher labor force participation rates than female youth. In both sets of countries, youth labor force participation rates are higher in rural than in urban areas.

The division between school and labor force participation is not always clearly identified. Many youth combine work and school, some at very early ages. Combining school and work can be detrimental to learning outcomes and could lead to early exit from school. For others, particularly older youth, combining school and work could provide job experience that could facilitate the transition of youth into employment after graduating from school.

# **V. Youth Unemployment**

The most commonly used measure of labor market difficulty used in developing countries is the unemployment rate. This indicator is typically monitored on a monthly basis in OECD countries, but is only available on a monthly, quarterly or even annual basis for a few developing countries. Our developing country unemployment measures are based on household survey data for various years between 1990 and 2004.

In every region the difficulty youth face in entering the labor market is evident in higher unemployment rates for young men and women than for older workers. Youth make up 25 percent of the working-age population worldwide, but 47 percent of the unemployed. The estimated global unemployment rate for youth increased steadily from 11.7 percent in 1993 to 14.4 percent in 2003.<sup>2</sup> Across all markets the youth

> 2 ILO (2004).  The ILO produces global and regional estimates based on several national surveys and the ILO Global Employment Trends model.

10
behavioral measures. The STEP data sets are a rich globally comparable information base that include direct and indirect measures (or proxies) for different types of skills as well as information on individual education, training and labor market trajectories and family background characteristics.

The results of this study have important implications for policy and programs focused on improving the education, training and skills profiles of workers to better suit the needs of the labor market. Our results show that skills yield significant payoffs in the labor market, even after controlling for education and other relevant individual and family background factors. This is particularly the case for our two measures of cognitive skills, reading proficiency and complexity of computers use on the job, while noncognitive skills show some significant, albeit small effects on earnings. Even after controlling for schooling, we find the association between earnings and reading proficiency (our first measure of cognitive skills) to be large and stable while the complexity of computer use on the job (our second measure of cognitive skills), has the largest association with earnings across the countries in our sample.

Beyond these overall findings, our results suggest that there is significant heterogeneity across countries in how skills are valued in the labor market. There is also some indication of possible subgroup differences by gender, employment status, and occupational group. These differences across groups highlight the need for additional analysis for a more nuanced understanding of the role of skills in labor market success and context-specific programming that takes into account the characteristics of the education and training systems and labor market needs of each country.

The paper is structured as follows. The next section (Section II) briefly describes the literature on the effect of skills on wages. Section III provides a description of the data and the analytic sample used in the empirical analysis. Section IV provides a discussion of the methodology. The results are reported in Section V, and Section VI provides conclusions.

# **II. Literature Review**

There is a vast econometric literature that examines returns to education and training as proxied by schooling (years of school or level of education completed) in high-, middle-, and lowincome countries (Card, 2001; Psacharopoulos and Patrinos, 2004; Hanushek et al. 2013). Most studies on returns to schooling are based on the standard Mincerian framework, which posits that schooling develops general skills and is thus a good measure of human capital (Mincer 1974). They estimate that each year of additional schooling is associated with an earnings increase of about 7 to 10 percentage points (Psacharopoulos, 1994; Psacharopoulos and Patrinos, 2004; 3
33 Figure 1: Autonomization of hospitals in selected districts <!-- Start of picture text --> 2003 2004 2005 2006 2007 2008<br>Hoan Kiem, Ha Noi Hai Ba Trung, Ha Noi<br>Vinh, Nghe An Cua Lo, Nghe An<br>Pleiku, Gia Lai Chu Pah, Gia Lai<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->
Operations Research 117: 326-339.

> [16] Comwell C, Schmidt P, and Sickles RC (1990) Production Frontiers with Cross

- -Sectional and Time Series Variation in Efficiency Levels. Journal of Econometrics, 46: 185-200.

- [17] Djerrari, FM, and Janssens, JG (1998) Overview paper on Institutional Options For Urban Water and Sanitation Services in Africa', WUP, Abidjan.

- [19] Eglal R, Rathgeber E and Brooks DB (1996) Water Management in Africa and the Middle East. Edited by Eglal R, Rathgeber E and Brooks DB

> [17] Farell MJ (1957) The Measurement of Production Efficiency. Journal of Royal Statistical Society, Series A, 120: 253-281.

> [18] Featherston AM, Langemeier MR and Ismet M (1997) A Non-Parametric Analysis of Efficiency for a Sample of Kansas Beef Cow Farms. Journal of Agricultural and Applied Economics, 29: 175-184.

> [19] Ferrier GP, and Lovell CAK (1990) Measuring Cost Efficiency in Banking-

- Econometric and Linear Programming Evidence. Journal of Econometrics, 46: 229-245.

> [20] Forsund FR, Lovell CAK, and Schmidt P (1980) A survey of Frontier Functions and of their Relationship to Efficiency Measurement. Journal of Econometrics, 13: 5- 25.

- [21] Greene WH, (1980) Maximum Likelihood Estimation of Econometric Frontier Functions. Journal of Econometrics, 13: 27-56.

- [22] Greene WH, (1997) Frontier Production Functions. In M. Pesaran and P. Schmidt, Handbook of Applied Econometrics: Volume II: Microeconomics. London: Blackwell Publishers.

> [23] Hausman, JA and Taylor WE (1981) Panel Data and Unobservable Individual

> Effects. Econometrica 30, 34-53.

> [24] Hunt LC, and Lynk EL (1995) Privatisation and Efficiency in the UK Water Industry: An Empirical Analysis. Oxford Bulletin of Economics and Statistics 57, 371-388.

> [25] Jondrow J, Lovell CAK, Materov IS and Schmidt P (1982) On the Estimation of

> Technical Inefficiency in the Stochastic Frontier Production Function Model.

19
percent). In all the other cases, the fertility rate and the infant mortality rates have negative parameters whereas public spending and income positive.

Moving now to assess the role of poverty, table 3 indicates that poverty levels indeed reduce enrolment rates. Depending on the specification being considered we find that a 1 percentage change in the poverty rate would translate into a decline in secondary enrolment rates ranging between .5 and 1.5 percentage points. That is, beyond being statistically significant, the magnitude of these estimates indicates that they are also economically significant. Thus this analysis indicates an additional channel through which poverty can affect growth.

**<u>Table 3: The impact of poverty on education</u>**

||D|ependent va|riable is th|e secondary|ll<br>_net_enro|ment rate||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Persistence|1.004|0.560|0.383|0.435|0.533|0.524|0.332|
|(lagged dependent)|_13.67_|_14.20_|_10.22_|_15.28_|_4.86_|_22.15_|_7.93_|
|Public spending on education|3.495|2.974|3.212|2.883|2.853|3.912|2.217|
|(% of GDP)|_2.31_|_4.98_|_10.51_|_13.68_|_4.71_|_10.38_|_7.17_|
|Fertility rate|||-5.562|-3.014||-6.622|-2.431|
|(births per woman)|||_-7.93_|_-3.33_||_-8.38_|_-1.67_|
|Infant Mortality||||-0.169|||-0.214|
|(per thousand live births)||||_-3.37_|||_-2.46_|
|Income|||||1.881|-3.878|3.557|
|(log GDP per capita)|||||_7.51_|_-1.60_|_2.29_|
|Poverty||-1.467|-1.121|-0.602|-1.392|-0.878|-0.521|
|(headcount)||_-8.33_|_-8.37_|_-3.61_|_-8.27_|_-6.35_|_-2.35_|
|Observations|163|105|105|99|105|105|99|
|Countries|73|55|55|53|55|55|3<br>5|
|Sargan test. p-val|0.18|0.24|0.35|0.78|0.20|0.67|98<br>0.|
|Second order correlation.p-val|0.58|0.25|0.13|0.12|0.26|0.13|40<br>1.|

Note: The table reports the results of regressing the secondary net enrolment rates on the variables in the first column. The data is in non-overlapping 5-year averages. Estimation method is GMM and the instrument set is the same in all the specifications and includes the lagged values of the dependent varaibles. The null hypotheses in the Sargan test of overidentifying restrictions and the test for second order serial correlation are (i) no correlation between the residuals and the instrument set and (ii) no second order correlation in the residuals (i.e. a large p-value indicates that there is no evidence against the null hypothesis).

# _IV.3 Inequality and output volatility_

In the remainder of this section we focus on a third rather unexplored channel: on how inequality can raise output volatility, which in turn is known to reduce growth. In fact, the last few years have witnessed a renewed interest in the relationship between macroeconomic volatility and economic growth (among others see Ramey and Ramey, 1995; Martin and Rogers, 2000; Fatas, 2002; Wolf, 2003; Hnatkovska and Loayza, 2004). To a large extent the main conclusion of these papers is that volatility and longrun economic growth tend to be negatively related.

25