rafmacalaba/datause-encoder-data huggingface dataset:
the modern sector is defined to consist of those enterprises covered by the survey, the informal sector and its heterogeneity automatically become invisible. The black-out is all the more effective if the coverage of the establishment survey is biased towards the urban sector: in sub-Saharan Africa, the vast majority of small industries are located in rural areas (Liedholm and Mead, 1986).

A second reason is that assessment through direct observation is difficult for those who do not have intimate contact with it. The truly marginal occupations of the street hawker and the shoeshine boy are all too visible, while the more productive activities are hidden away from the main streets and behind closed doors. As the 1972 ILO report put it, "from the vantage point of central Nairobi, with its gleaming skyscrapers, the dwellings and commercial structures of the informal sector look indeed like hovels. For observers surrounded by imported steel, glass and concrete, it requires a leap of the imagination and considerable openness of mind to perceive the informal sector as a sector of thriving economic activity and a source of Kenya's future wealth".

A further reason is that the human mind finds it easier to think in terms of classifications than continue, and prefers classifications with a small number of categories to those with many. Once the dual economy literature had given credence to a modern/informal dichotomy, it was very hard to dislodge.

Why has the reappraisal of the small enterprise been so important? First, it has legitimized efforts to stimulate employment in what is now often known, not as the informal, but as the traditional, sector. For example, the advocates of vocational education now include training for self-employment as well as employment among its objectives. Second, and more importantly, it has stimulated an interest in how skills are developed and passed on in traditional sector.

# 2.5 Recognition of indigenous aRDrenticeship

A side-product of the recognition of the role of the small enterprise in many countries was the recognition of the existence of an indigenous apprenticeship system. The first early accounts (for example, Lloyd, 1953; Callaway, 1964; Peil, 1966; Faminu and Koch, 1967; Koll and Lajunji, 1967) were regarded as the work of pioneering explorers, and in a sense they were, but only for the literature on economic development. In retrospect, one may now well ask why anyone did not expect to encounter indigenous apprenticeship as a matter of course. In nearly every society a small proportion of the population has been employed in occupations other than farming and trade, some requiring specialized skills. How else, in the absence of institutional training, could these skills be maintained, except by personal transfer from one generation to the next via some form ofapprenticeship? The Western writers were weil aware of the centuries-old tradition of apprenticeship in Europe. Did they really not expect to find it equally thriving elsewhere?

- 11 -
simulate a hydrological cycle of different intensity. In Precis there is more rainfall with a lower than observed intensity, whereas in MM5 there is more rainfall with a higher than observed intensity (Tadross et al., 2005).

Because future projections were expressed in monthly rainfall amount, it was necessary to downscale the projections to dekads of rainfall amount. Downscaling was based on the empirical rainfall distribution of the Chitedze station. In detail, based on the historical rainfall amount for each dekad between 1961 and 2005, the mean rainfall amount for each dekad was calculated and transformed into a percentage of monthly rainfall amount. This percentage was used to distribute the monthly rainfall amount of the future projections into rainfall amount in dekads. Hence it is assumed that future rainfall patterns on average are the same as in the past. While this assumption is questionable, other approaches are not reliable due to lack of data. Furthermore, because the insurance trigger events are constructed for at least 3 dekads for each phase, changing distribution patterns within dekads are of minor interest from an insurance perspective. In this context the variance is of more importance. Figure 5 shows the mean distribution calculated from the empirical data as well as based on the MM5 and Precis models for the whole season (season starts at the beginning of August).

<!-- Start of picture text --> 100<br>90 MM5 future<br>80 Precis future<br>70 Empirical<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Dekads<br>1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35<br>Rainfall amount (mm)<br><!-- End of picture text --> Fig. 5: Mean accumulated rainfall per dekad for the empirical data as well as the MM5 and PRECIS Scenarios Source: Based on data from Tadross et al. 2005.

The MM5 future projections show lower rainfall in the beginning of the season compared to the mean empirical estimates. For the Precis model higher rainfall appears to characterize the beginning of the season. On the other hand, a trend with lower rainfall at the end of the season seems to emerge for Precis compared to the empirical data. Usually, one would use the future projections to estimate the future parameters for each dekad and model. However, due to data limitations, e.g. the projections are only point estimates, especially for estimating the standard deviation around the dekads, two different 11
significantly to 20 percent. If the APEC grouping is considered, the aggregate share jumps to 58 percent but the range of individual country shares within the group is extremely wide - from a low of 0.09 percent for Vietnam to a high of nearly 30 percent for the U.S.

Of course, the presence of the smaller Asian countries in a group is more significant the smaller the grouping. This is readily apparent in the columns showing the percent of APEC GDP and the percent of ASEAN GDP. In APEC, the U.S. makes up 51 percent of the group GDP, while the ASEAN countries (those that are also members of APEC) make up only 3 percent on a combined basis. Within ASEAN-Plus-Three (APT), the share of the ASEAN countries in the group's aggregate GDP rises to 8.6 percent. Japan has by far the largest share at 69 percent while the shares of China and Korea are only 16 percent and 6 percent, respectively.

Comparing the GDP levels of the three East Asian groupings with those of NAFTA and the EU highlights differences in economic development more than in group size. In total GDP, APEC is the largest of all the regional groupings ($18 trillion or 58 percent of world GDP) reflecting, of course, the $9 trillion GDP of the U.S. The 20 percent global share of ASEAN-Plus-Three, however, is not a great deal less than the EU's 27 percent or even NAFTA's 33 percent. ASEAN's share, on the other hand, is very small (1.75 percent) when compared to these other regions.

Another important comparison is that with the Free Trade Area of the Americas (FTAA), which is a megaregional trade arrangement (MTA)1<sup>40</sup> slated to ultimately (in 2005) include 34 countries in the Western Hemisphere. Table S.3 in the Statistical Appendix shows GDP and GNI of the countries included in this group. The FTAA's share of global GDP (1999) at 38 percent is greater than that of ASEAN-Plus-Three (20 percent), NAFTA (33 percent) and the EU (27 percent), although considerably smaller than that of APEC (58 percent). Here again, however, the major share belongs to the U.S., without which the FTAA's share would be only 8.5 percent compared to APEC's considerably higher 29 percent (excluding the U.S.). This reflects the inclusion in the FTAA of the many developing economies in Latin America and the Caribbean.

The heterogeneity of economic development within East Asia can also be seen in the structure of output (GDP), which for 1999 is shown in Figure 2.1 (value added as a percent of total GDP for agriculture, industry and services.) (Complete data for 1990 and 1999 are shown in Table S.4 in the Statistical Appendix.) As might be expected, the newer (and lesser developed) members of ASEAN, in particular Cambodia, Lao PDR and Myanmar, are more reliant on agricultural production. 41 For Singapore, the Philippines, Thailand and Vietnam, services provide the greatest contribution to output, while for Indonesia and Malaysia, the largest share comes from industrial production.

36
GHGE.<sup>13</sup> The OECD IOTs contain information about how each sector’s gross production is distributed among all economic sectors (intermediate demand) and the final demand. The latter comprises households’ domestic demand, non-profit institutions’ domestic demand, government’s domestic demand, capital formation, change in inventories, exports, and imports. The sector classification corresponds to 2-digits of the 4<sup>th</sup> revision of the International Standard Industrial Classification of Economic Activities (ISIC Rev. 4), with some sectors grouped according to their nature. This information generates the matrix of coefficients and the vector of final demand. The IOTs matrix has the structure described in Table 3: i represents the demand from sector _j_ for production of sector _i_ , represents the added value generated by sector _j_ , and represents the gross production of sector _j_ . Notice that the sum ii yy vv of the intermediate demand plus added value equals the sector’s gross production. Then, the technology ii YY coefficients are calculated by dividing each sector’s intermediate demand from a specific sector by that sector’s gross production. In other words, each one of the technology coefficients represents the ratio i . In this paper, information about imports is not included in the final demand vector since they do not relate directly to the production process of each sector. Then, only domestic demand and exports are ii yy /YY considered for the estimations.

**Table 3 – Input-Output table structure**

||**_Sector 1_**<br>|**_Sector 2_**<br>⋯<br>|**_Sector n_**<br>|**_Final demand_**<br>|
|---|---|---|---|---|
|**_Sector 1_**|y11<br>|y12<br>⋯<br>|y1n<br>|f1<br>|
|**_Sector 2_**|y21|y22<br>⋯|y2n|f2|
|⋮|⋮<br>|⋮<br>⋮<br>|⋮<br>|⋮<br>|
|**_Sector n_**|yn1<br>|yn2<br>⋯<br>|yn<br>|fn|
|**_Added Value (V)_**|v1<br>|v2<br>⋯<br>|vn<br>||
|**_Gross product (Y)_**|Y1|Y2<br>⋯|Yn||

Source: Own elaboration The third source of information, the Climate Watch/CAIT series, contains information about the level of GHGE by industry and end-uses from 1990 to 2018, following the Intergovernmental Panel on Climate Change (IPCC) reporting framework. Table 4 presents the sectors included in CAIT and the activities they

> 13 Available in https://www.climatewatchdata.org/ghg-emissions?end_year=2018&start_year=1990.

9
# **MEASURING THE ECONOMIC IMPACT OF CLIMATE CHANGE ON ETHIOPIAN AGRICULTURE: RICARDIAN APPROACH**<sup>**1**</sup>

# **Temesgen Tadesse Deressa**<sup>**2**</sup>

> 1 An earlier version of this Working Paper was published as CEEPA Discussion Paper number 25.

> 2 Ethiopian Development Research Institute (EDRI), e-mail: <u>ttderessa@yahoo.com.</u>

> This study is sponsored by the GEF and World Bank. It is part of an Africa-wide study on the economic impact of climate change on agriculture coordinated by the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria and Yale University. The author would like to thank Professor Rashid Hassan, Dr James Benhin, Dr Pradeep Kurukulasuria, Professor Robert Mendelson, Professor Arial Dinar, Dr Kidane Georgis and Ato Abebe Tadege.
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<!-- Start of picture text --> Korea, Rep.<br>60 18<br>29 pat 16<br>50 te ooh 14<br>45 o fe : 2<br>. soeet te 8<br>30 ° “eet %e, . 6<br>243 “eee<br>2042 4<br>15 48 2<br>105 -+: 0-2<br>0 godtBSSTARFRTLSESTIIHAIFRTEQooedceoceoeeceeOddtiAnococoeoncaeaaeoncocecceceocaeAAI NNNNNNNNNNNNNNdd dtd dt dt ddd dt dtdti tdtttttcdlddodNNN 4<br>= S&P Rating «eee Relative Rating [Right scale]<br><!-- End of picture text -->
iors are deeply influenced by cultural norms. There is a policy lesson here concerning the freedom of access provided to parents to take part in the school life. Sometimes schools tend to be insular places without much scope for parents to contribute, but measures to harness parents’ contributions in their time as well as in cash and kind may yield positive results.

**Teachers** : Teachers are widely recognized to be the most important factor in many studies of student achievement. Yet, in this case, the inclusion of a number of teacher related variables does not appear to be useful in explaining Vietnam’s achievement gap. It is interesting to note that in the regressions, the variables individually tend to have statistically significant coefficients, but do not affect the dummy coefficient. One teacher variable, the proportion of certified teachers, is clearly economically and statistically significant, and it is one where Vietnam has an advantage (80% vs. 68% certified). Variables that relate to the use of student assessment in providing feedback to teachers on their performance are seen to be important. The presence of other assessment and feedback related variables are also in line with intuition. It is possible that the advantages which Vietnam enjoys with regard to teachers are ‘swamped’ by the effects of variables for which Vietnam does not have an advantage, so the net result is that the gap is not explained by PISA related teachers variables. It is also possible that the effect of teachers is particularly context specific, revealing a weakness of the pooled regression approach of Fryer and Levitt. This last explanation is further investigated in the next section of the paper.

**School** School resources matter with regard to PISA results in the international perspective, as the scatter plot in Figure 1 motivating this paper clearly shows. Developing countries with the notable exception of Vietnam are clustered at the bottom left hand side. And there is a positive slope with high scoring countries tending to be on the higher income side. In this section, we see that the effort made by the Vietnamese government to invest in education plays an important part in explaining the achievement gap. Even though Vietnam may be poor with regard to per capita income, it is not as poor with regard to the quality of educational resources and the quality of physical infrastructure. This can be seen in Figure 4 comparing the average PISA 2012 mathematics scores with a school infrastructure quality index. Compared to Figure 1, where Vietnam is the country with the lowest GDP per capita in the PISA 2012 data set and thus placed in the left hand side of the figure, Vietnam moves more towards the middle in Figure 4. Using SCMATBUI (quality of school infrastructure), Vietnam jumps ahead 11 places, with a similar story (not shown) regarding SCMATEDU (quality of educational materials). A key reason is the investments by the Vietnamese government in schools in smaller towns and rural areas as classified by PISA, given that the dispersion of school infrastructure is lower in Vietnam compared to other countries.

27
constructed from a migration history to school enrollment. If the survey includes a detailed employment module, brain waste can be studied from the source-country perspective by examining job types held by individuals in different educational and experience cohorts. A survey that oversamples migrant households could be particularly informative in studying either brain drain or brain gain, as the distribution of educational attainment among migrants would be measured with less error. On the other hand, for learning about the brain-drain issue from the perspective of specific occupations, a specialized survey targeted at individuals with specific types of training (e.g. nursing school) would be more informative.

# **Trade, Foreign Direct Investment and Migration**

The main premise of this area of literature is that migration can facilitate integration between countries in other areas, mainly through trade and foreign direct investment (FDI) flows. Migrants possess knowledge of specific conditions, regulations and product features in both import and export markets. By using their knowledge, they can lower transactions costs, match parties in a relationship and increase economic integration. Migrants also are consumers of specific products from their home countries. Starting with Rauch (1999), an extensive literature mentioned these information and demand channels as key to the relationships between trade, migration and FDI flows.

The main analytical framework consists of gravity models and focuses on OECD countries due to data availability. Most papers analyze a single country and its multiple trading partners (such as Head and Ries, 1998; Dunlevy and Hutchinson, 2001; Girma 38
**6 Acknowledgments**

The authors want to thank Nicolas Naville and Fanny Henriet for their assistance in this research, and Patrice Dumas, Celine Guivarch and Julia Kowalewski for their comments on a previous version of this paper. This research is supported by the European Community’s Seventh Framework Program (FP7/2007-2013) through the WEATHER project (http://www.weather-project.eu).

# **7 References**

Albala-Bertrand, J. M., 1993. The Political Economy of Large Natural Disasters with Special Reference to Developing Countries, Oxford: Clarendon Press.

Anderson, C. W., Santos, J. R., Haimes, Y. Y., 2007. A risk-based input-output methodology for measuring the effects of the august 2003 Northeast black-out. Economic Systems Research 19 (2), 183-204.

Battiston, S., Delli Gatti, D., Gallegati, M., Greenwald, B., Stiglitz, J., 2007. Credit chains and bankruptcy propagation in production networks. Journal of Economic Dynamics and Control 31, 2061-2084.

B ́enassy, J.P., 1993. Nonclearing markets: Microeconomic concepts and macroeconomic applications. Journal of Economic Literature, 31:732-761, 1993.

Bockarjova, M., Steenge, A., Van Der Veen, A., 2004. On direct estimation of initial damage in te case of major catastrophe; derivation of the basic equation. Disaster Prevention and Management 13 (4), 330336.

42
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17 tightly to income and decoupled from fuel consumption. The welfare gains attributable to the HUS are thus the same, but opposite in sign, as those of removing it entirely. One key feature in common to the three metrics is that we examine their distribution across income quartile-gas consumption quartile cells.

For simplicity and due of data limitations, attention is restricted to those households that use natural gas for space heating (some 58% of all HBS households), to the gas HUS, and to the data from 2017 and 2018, when a one-part tariff with constant price per unit of gas was in place, and the price was virtually constant over time, which allows us to compute quantity demanded from the expenditure and HUS data reported in the HBS.

In order to compute the consumer surplus, we must first estimate a demand function. Ideally, we wish to estimate = (2) mm mm mm mm mm llBB QQ αα+ ββ∙llBB PP + γγ∙llBB hB ii + xx δδ+ εε where Q is gas consumed during the year, P is the price of natural gas, hinc is household income, **x** is a vector of dwelling, household and location characteristics, and β and γ are the price and income elasticities of demand, respectively. However, the tariffs were stable over 2017 and 2018, and the share of households who received “privileges” (and hence discounted gas tariffs) during those years is only 6-7%, which means that we lack the variation necessary to fit demand function (1) (see bottom row of table 10). We get around this problem using two possible approaches.

Approach 1 assumes that the gas HUS acts as a price subsidy. Under this assumption, the ∗ <u>(</u> ∙Q −g <u>)</u> price per cubic meter of gas effectively paid by the household is = ~~,~~ where P is PPii ii ssg gg PPmm QQii the posted price or the discounted posted price if the household is entitled to privileges. Clearly, ∗ is equal to when the household does not receive the HUS, and it will be lower than mm mm PP PP
most important variables identified during the modeling process (See Table A3.1 in Annex 3 for an evaluation of the two models).

Table 3. National PMT OLS Regression of Log Per Capita Household Consumption

|Nati|onal|
|---|---|
|Variables<br>Estimate|Std. Error|
|Household size: 2 members<br>-0.432***|(0.037)|
|Household size: 3 members<br>-0.700**|(0.036)|
|Household size: 4 members<br>-0.902***|(0.036)|
|Household size: 5 members<br>-1.061***|(0.037)|
|Household size: 6 members<br>-1.181***|(0.037)|
|Household size: 7+ members<br>-1.379***|(0.037)|
|Dependency ratio<br>-0.090***|(0.015)|
|Max. school level: less than Secondary 4<br>-0.027***|(0.009)|
|Max. school level: less than Primary<br>-0.042***|(0.01)|
|Livelihood source: farming, aid, other<br>-0.028***|(0.007)|
|Head: female<br>-0.077***|(0.009)|
|Rooms per household member<br>0.055***|(0.007)|
|House material: mud, wood, straw, tent, or incomplete<br>-0.093***|(0.009)|
|Floor material: earth<br>-0.066***|(0.013)|
|Lighting source: grass, firewood, or none<br>-0.063***|(0.008)|
|Drinking water source: not common network<br>-0.029***|(0.009)|
|Cooking fuel: cow dung, grass, or does not cook<br>-0.134***|(0.009)|
|No motorcycle<br>-0.055***|(0.017)|
|No motor vehicle<br>-0.221***|(0.016)|
|No radio<br>-0.055***|(0.008)|
|No computer<br>-0.174***|(0.022)|
|No refrigerator<br>-0.073***|(0.012)|
|No television<br>-0.051***|(0.011)|
|No AC<br>-0.055***|(0.016)|
|No mobile phone<br>-0.147***|(0.008)|
|Intercept<br>10.727***|(0.046)|
|Obs.<br>11,947||
|Adj. R-Squared<br>0.579||
|_Notes:_Variable definitions and reference categories for categorical variables are pre<br>Table A1.1. in Annex 1. A small percentage of missing categorical and continuous ob<br>were imputed by modes and means, respectively. *** p<0.01, ** p<0.05, * p<0.10.|sented in<br>servations|

Furthermore, state and area (e.g., rural and urban) indicator variables, as well as employment variables, were excluded from the final model, since welfare programs in Sudan will be administered at the state 13