rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4. Middle Eastern Countries' Thirty Largest Three-Digit Non-Energy Product Exports to OECD Countries, 1992.**

|||Value of Exp|orts ($million)|||**Percenit**of To<br>|tal Exports(%<br>|)|
|---|---|---|---|---|---|---|---|---|
|Description (SITC)|1970|1980|1986|1992|1970|1980|11986|1992|
|Clothing not of fur (841)|43.5|413.4|1,620.9|6,064.9|2.4|4.2|11.1|21.1|
|Pearls and precious stones (667)|139.2|1,174.0|1,771.6|2,817.7|7.7|11.8|12.2|9.8|
|Fresh fruit and nuts (051)|258.8|904.9|919.2|1,231.3|14.3|9.1|6.3|4.3|
|Organic chemicals (512)|6.3|205.6|736.1|1,103.3|0.3|2.1|5.0|3.8|
|**Floor**coverings (657)|125.5|676.5|474.3|856.4|7.0|6.8|3.3|3.0|
|Telecommunications equipment 724)|**5**7|58.3|136.4|839.3|0.3|0.6|0.9|2.9|
|Gold and silver jewelry (897)|1.4|123.7|278.9|582.1|0.1|1.2|1.9|2.0|
|Aluminum (684)|0.4|148.6|442.5|568.1||1.5|3.0|2.0|
|Fruit preserved (053)|38.2|i78.4|259.6|554.2|2.1|1.8|1.8|1.9|
|<br>1Textileyarn and thread (651)|44.5|344.9|499.4|552.4|2.5|3.5|3.4|1.9|
|<br>Scientific instruments (861)|**3.4**|57.6|141.1|531.3|0.2|0.6|1.0|1.8|
|Plastic materials (581)|3.3|48.1|357.7|526.3|0.2|(.5|2.5|1.8|
|Nonelectric machinery (719)|6.5|89.0|161.8|4' 9.6|0.4|0.9|1.1|1.7|
|Nonelectric power machinLry (711)|13.1|276.7|324.0|498.4|0.7|2.8|2.2|1.7|
|Fresh vegetables (054)|63.5|232.8|220.1|467.6|3.5|2.3|_1.5_|1.6|
|Office machinery (714)|2.4|33.2|143.7|**439.4**|0.1|0.3|1.0|1.5|
|<br>rohacco unmanutactured (121)|74.6|204.8|314.1|431 7|4.1|2.1|2.2|1.5|
|<br>Fllectrtcal machinery, nes**(729)**|4.7|98.5|272.9|358.5|0.3|!.0|1.9|1.2|
|<br>Driedfruit(0521<br>|35.1|!95.2|183.6<br>|;<br>345.7|1.9|<br>2.0|<br>1.3|1.2|
|1Textile products nes (656)|0.7|59.2|113.0|333.0||0.6|0.b|1.2|
|Manufactured fertilizers (561)|16.6<br>|j<br>122.2|283.2|315.6|0t.9|1.2|1.9|1.1|
|Woven textiles noncotton (653)|-7.4|21.4|41.4|302.7|0.4|0.2|0.3|1.1|
|Crudevegetablematerials (292)|30.7|209.5|218.4|30i.9|1.7|2.1|1.5|1.0|
|Cotton fabrics woven (652)|13.2|54.7|126.8|267.2|0.7|0.6|0.9|0.9|
|!Oiher crude minerals (276)||<br>2.5|166.6|202.8|246.3|1.2|1.7|1.4|0.9|
|<br>Coroan(263)|<br>292.3|565.6|461.8|241.3|16.2|5.7|3.2|0.8|
|<br>Electric power machinery (722)|2.1|39.5|77.0|239.4|0.1|0.4|0.5|0.8|
|11 Electrical Dis,ributing machinery; (723)<br>|0.1|5.9|32.6|236.9|--|0.1|0.2|0.8|
|iRubber articles nes (629)|10.1|52.5|76.4|215.0|0.6|0.5|_0.5_|0.7|
|**Road**moor vehicles and parts (732)|1<br>i.5|22.2|46.3|204.6|0.1|0.2|0.3|0.7|
|.<br>TOTAL OF ABOVE PRODUCTS|1,266.4|**6**|<br>,783.4<br>||<br>10,937.6|22,172.0|70.2|68.3|75.0|77.1|

,ource: United Nations Series D. Trade Tapes. The above statistics exclude aircraft and special transactions recorded in SITC 931.

C
school attendance was actually higher in regions controlled exclusively by the IRG than in regions where the IRG controlled part of the governorate (the regions on the front battle lines with the most violence) and regions controlled exclusively by DFA forces (which also had more violence on average than IRG-held regions). However, attendance in IRG-controlled regions only became worse than more conflict-affected areas after the beginning of the teacher strike.

In addition to demonstrating the strike had a strong impact on school attendance, we further compare the impact of the strike to the impacts of the shocks to the severity of violence described above. Table 6 estimates the following variant of specification (2): where all variables are as above, _j_ denotes the four shocks to school attendance, and _Durin_ _~~g~~ Shock_<sup>_j_</sup> is an indicator equaling one if shock _j_ was occurring during month _m_ and year _y_ . The specifications include all surveys over the course of the 12 months conducted during the school year and separately estimates the impact of each shock.

In all specifications, the impact of the teacher strike continues to the have the largest impact on school attendance and is the most precisely estimated coefficient. Columns (1)-(4) estimate the impact each shock has individually using the entire sample as a comparison group; and columns (5)-(7) estimate the full specification comparing the impacts of each of the shocks. Columns (1)-(4) again demonstrate that only the strike has a large and unambiguous impact on school attendance, and that one cannot reject the hypothesis that the shocks to the severity of violence had no impact on school attendance at standard significance levels.

When comparing the impact of each shock directly in column (5), one can reject the hypothesis that the impacts of all three shocks were equal at conventional significance levels (p-value of 0.016). The results in column (5)- the stronger and more precisely estimated impact of the strike, and the statistical difference between each of the four shocks having an equal impactsurvive a number of different specifications. Column (6) demonstrates the same pattern using a logit specification; and when dropping governorates where it was reported that not all districts joined the strike in column (7), the magnitude and the precision of the estimated impact of the strike increases.

# **Section 6. Example Illustrating the Importance of Incorporating All Conflict Shocks**

As has been demonstrated above, oftentimes active conflict is a tumultuous environment that impacts access to education through many different channels. However, the results described above have two separate implications for understanding the relationship between conflict and education more generally. First, one of the many reasons why the impact of living in close proximity to violence has on education might vary significantly across contexts is the fact that there are conflicts in which the relationship might not be universal over the entire 19
I POLICY RESEARCH WORKING PAPER 2224

# Summary findings

Kraay and Monokroussos consider two alternative methods of forecasting real per capita GDP at various horizons:

* Univariate time series models estimated country by country.

* Cross-country growth regressions. They evaluate the out-of-sample forecasting performance of both approaches for a large sample of industrial and developing countries.

They find only modest differences between the two approaches. In almost all cases, differences in median (across countries) forecast performance are small relative to the large discrepancies between forecasts and actual outcomes.

Interestingly, the performance of both models is similar to that of forecasts generated by the World Bank's Unified Survey.

The results do not provide a compelling case for one approach over another, but they do indicate that there are potential gains from combining time series and growth-regression-based forecasting approaches.

- - This paper a product of Macroeconomics and Growth, Development Research Group is part of a larger effort in the group to improve the understanding of economic growth. Copies of the paper are available free from the World Bank, 1818 H Street, NW, Washington, DC 20433. Please contact Rina Bonfield, room MC3-354, telephone 202-473-1248, fax 202522-3518, email address abonfield@worldbank.org. Policy Research Working Papers are also posted on the Web at http: //www.worldbank.org/research/workingpapers. The authors may be contacted at akraay@worldbank.org or gmonokroussos@worldbank.org. November 1999. (32 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
**Table 2:  Product categories accounting for most manufacturing exports**

|||Top 3 ca<br>|tegories<br>|Categories that<br>for most expo<br>|jointly account<br>rts(85-90%)<br>|
|---|---|---|---|---|---|
|Broadgroups|HS 2-digit|Large|Small|Large|Small|
|Textiles|50-63|||1||
|Animal products|1-5|||2||
|Foodstuff|16-24|4||6||
|Mineral products|25-27|4|1|7|1|
|Footwear & raw hides, leather|41-43, 64-67|2|2|9|3|
|Vegetable products|6-15|8|3|12|5|
|Plastics & Rubber|39-40|2|4|6|7|
|Metal|72-83|4|2|8|10|
|Chemicals|28-38|3|7|7|10|
|Furniture & wood products|44-49, 94||6|3|11|
|Machinery|84-87||2|1|13|
|Stone &glass|68-71|||1|3|
|No. of categories||27|27|63|63|

Notes: Small exporters are firms exporting at least 1,000 USD, but not more than 100,000 USD per year. The statistics show the total number of product categories over the nine years in the sample, i.e. 3 categories in each year for a total of 27 categories in the first two columns. In the last two columns, we used a slightly lower threshold in each year to define “most exports” for large exporters (85%) than for small exporters (90%) in order to obtain the same total number of categories (63).

Even including more than twice as many products and accounting for approximately 90% of all exports, it is still the case that some broad product groupings are almost only exported by large exporters, in particular Foodstuff, Mineral products, and Footwear & leather. The almost total absence of Textiles and Animal products is also notable, given the agrarian orientation of the economy and the focus on light manufacturing. Small exporters dominate in rather different product categories, e.g. Furniture and Stone & glass, but also in Machinery, which we did not expect to see on this list. We have to keep in mind, however, that export volumes are extremely small, reaching only an aggregate of around 400,000 USD per year for small exporters. Hence, a second-hand car dealership that exported a few vehicles per year to neighboring countries would appear on this list.

Of course, specialization is not complete and there is overlap for some products. It is not unlikely that products exported by both large and small firms are exactly the type of products where Rwanda has some chance of integrating in GVCs. The most important categories where there is notable overlap for small and large exporters are Metalworking, Chemical products, and Vegetable products. With the exception of the last category, this list is not really as expected. The next categories are Plastics & rubber, Furniture, and Footwear, which are perhaps less surprising.

# **4.6 Firm-level characteristics of exporters**

We have documented the evolution and the nature of Rwandan exports in detail. To assess the potential for further export growth, it is also important to have a sense how different current exporters are from firms that currently do not export. If they are radically different, it would be implausible that many non-exporters could enter the export market. We will also 23
test (Zimmerman, 1997). This is a method widely used to examine the effect of interventions. It is a hypothesis testing procedure for answering questions about means when data are drawn from two random samples of independent observations or from the same sample with repeated observations. In our case we have the same households participating in both rounds and each time participating in CCTs for both _Mirt_ and traditional stoves. This statistical method is in principle very appropriate, though it is a parametric test and assumes an underlying normal distribution.

We run normality tests for the two outcome variables. Because fuel consumption is non-normally distributed, we augment the two-sample t test with the non-parametric two-sample Wilcoxon rank-sum (Mann-Whitney) test (Wilcoxon, 1945). These results are very similar to those from the paired difference tests and are included in Appendix B. We also analyze the determinants of fuelwood savings using regression analysis. Since we expect that the error terms for households in the same village might not be independent, we cluster standard errors at village level.

The variables related to household and cook characteristics, including age, marital status, education and occupation of cook, and family size, are obtained from a household survey conducted shortly after the _Mirt_ stove was made available to respondents. Variables generated from the CCT include specific fuelwood savings, changes in cooking time and environmental factors, such as ambient temperature and wind conditions at the cooking site during the CCT. Both groups of variables are included as controls in the regression. Summary statistics of variables used in the regression are provided in Appendix F.

# **4** **_._ Results**

## _4.1 Stove Performance_

To evaluate differences in fuelwood to cook the standard quantities of _injera,_ we carry out three two-sample t tests. The first test utilizes the pooled data (i.e. for the two rounds together). The second test is by round and the third is by region.

As shown in Table 1, using the pooled data we find that the _Mirt_ stove on average uses 291grams less fuelwood (26%) per kg of food cooked than traditional stoves and this 13 | P a g e
**Figure 30: Sales losses due to power outages and firms with generators**

<!-- Start of picture text --> Median sales losses due to power % of firms with generator<br>outages<br>Malawi Kenya<br>Tanzania Tanzania<br>Madagascar Malawi<br>Kenya Zambia<br>Zambia Uganda<br>Uganda Madagascar<br>South Africa South Africa<br>SSA SSA<br>Low income Low income<br>0% 2% 4% 6% 8% 10% 0% 20% 40% 60% 80%<br><!-- End of picture text --> _Source: Malawi Investment Climate Assessment, 2006_ Quality of electricity supply was cited as the forth most serious obstacle to growth by firms in the ICA (World Bank, 2006) and as the primary obstacle given by the 2008 Malawi business climate survey conducted by the Malawian Chamber of Commerce. Additionally there have been specific investment decisions negatively influenced by electricity supply. Inconsistent quality of supply was cited by cigarette manufacturer BAT for moving its operation out of Malawi; the proposed extraction of titanium heavy sands from the lake was cancelled for the same reason; and similarly several potential textile investments were never realized. Power outages damage product quality in the dairy industry, require extensive cleaning of equipment in the plastics industry, and result in high losses in thread spinning (where consistent power is required to produce even width of threads). Firms go to expensive lengths to get around the constraint. Paladin, a mining company in the process of commissioning the uranium mine at Kayelekera, is using diesel generators at a operating cost greatly higher than the industrial electricity tariff.

The state electricity company is currently unable to provide a reliable source of power to businesses resulting in significant losses and deterring investment in power sensitive industry. The lack of reliable power supply significantly lowers social returns and hence is likely to be a binding constraint to growth.

# **Irrigation and Rainfall**

The vast majority of agricultural production is rain-fed. Historically Malawi has experienced serious droughts (1992, 1994, 1997, 2002, 2005) and floods, both of which have impeded agricultural productivity, damaging both agricultural land and crops. This rainfall variability is thought to have intensified risk-averse behavior by farmers and investors in agricultural industries and services, limiting diversification into other cash crops.

How much does rainfall contribute to economic growth? Although a total drought clearly collapses all agriculture (and hence GDP) the data suggests that different rainfall requirements of different crops probably diversifies the economy’s overall sensitivity to a

- 34 -
# **7. Changes in support for education reforms over time**

We next investigate whether the regularities we uncover by analyzing the sample of LiTS 2023 existed in the earlier rounds of the survey.<sup>10</sup> Expectations of future mobility or support for longterm reforms may have fallen in the wake of the COVID-19 pandemic, which had profound and long-lasting impacts on economic activity and household welfare (World Bank 2022). We use data from the 2010 and 2016 rounds of the LiTS to examine the stability of the linkages between expectations of mobility and support for education reforms over time.

The 2010 and 2016 survey rounds offer two contrasting data points. Like the 2023 round, the 2010 round was conducted in the wake of the Global Financial Crisis and the European debt crisis of 2009/10. In contrast, the 2016 data were collected during the period of relative economic stability in the region.

Table 5 presents estimations for three rounds of the LiTS (2010, 2016, and 2023).<sup>11</sup> The first three sets of results demonstrate that, as in 2023, respondents with more optimistic expectations about their economic mobility were more likely to support tax-financed education reforms in 2010 and 2016. The strength of this relationship doubled from 0.007 in 2010 to 0.014 in 2016, remaining virtually unchanged in 2023. The effect on support for reforms of expectations of intergenerational mobility increased from 0.054 in 2010 to 0.073 in 2016 and decreased to 0.041 in 2023. In all three survey rounds, beliefs about the fairness of the underlying economic environment and attitudes toward risk remain significant correlates of support for tax-financed education reforms.

Two findings emerge from these intertemporal comparisons. First, the positive association between intra- and intergenerational expectations of upward mobility persists for over a decade and during periods of economic turmoil and relative calm. Second, the relationship between

> 10 The Chow (1960) test on structural break indicates that for all pairwise comparisons of three rounds of LiTS, estimates based on pooled samples are rejected in favor of separate estimates.

> 11 The results for the 2023 round of LiTS in Table 5 differ from the results shown in Table 2, because here we use the monthly consumption aggregate as a control variable whereas the results in Table 2 are based on a specification with monthly income as control variable. Information about monthly income was collected only in the 2023 round.

15
**Table 1**

# **Social** and Economic btructure and Inflation **in East Asia and Latin America**

|U|rban||Share of Lab|or|||
|---|---|---|---|---|---|---|
|<br>|Population/<br>Total|Industryl<br>GDP (1960)|Force in<br>Unions|Income:<br>Lowest Fifth/|Average<br> <br>Infla|Annual<br>tion|
||(1960)||(c. 1975)|Highest Fifth|1965-|1980-|
||||||1980|1986|
|Argentina|71?|38Z|25Z|11.4|78.32|326.2?|
|Brazil<br>|45|35|na|33.3|31.3|157.1|
|Chile|69|38|25|11.4<br>|129.9|20.2|
|Colombia|47|26|13|21.1|17.4|22.6|
|Costa Rica|34|19|12|16.6|11.3|32.3|
|Jamaica|30|38|25|na|12.8|19.8|
|Mexico|50|29|20|15.1|13.1|63.7|
|Peru|47|29|37|32.1|20.5|100.1|
|Uruguay|73|28|25|10.8|57.8|5C.4|
|Venezuela|68|22|45|18.0|8.7|8.7|
|Mean, Latin|||||||
|America|53.4|30.2|25.2|18.9|38.1|80.1|
|Indonesia|15|17|10|na|34.3|8.9|
|Korea|28|19|10|7.5|18.8|5.4|
|Malaysia|26|18|15|16.0|4.9|1.4|
|Philippines|30|28|na|13.6|11.7|18.2|
|Taiwan|35|29|11|4.2|8.3|5.8|
|Thailand|13|19|na|8.9|6.8|3.0|
|Turkey|30|21|8|na|20.7|37.3|
|Mean, Asia|25.3|21.6|10.8|10.0|15.1|11.4|

(Note: means are unweighted averages. Data on income distribution is from household surveys in the late 1960s or early 1970s. Industry share of GDP is probably biased upwards for the Latin AmL can countries because of higher levels of protection.

Sources: World Bank, World Development Report 1978 (Washington D.C.: World Bank, 1978); World Bank, World Bank Development Report 1988 (New York: Oxford University Press, 1988); Republic of China, Council on Economic Planning and Development, Taiwan Statistical Data Book 1988 (Taipei: CEPD, 1988); Charles Lewis Taylor and David Jodice, World Handbook of Political and Social Indicators (New Haven: Yale University Press, 1983); Jeffrey Sachs, 'Social Conflict and Populist Policies in Latin America,w National Bureau of Economic Research Working Paper No. 2897 (March 1989).

8
**<u>Table 3: Impact of reform on threats to tenure security</u>**

||**Any rea**|**llocation**|**Any expr**|**opriation**|
|---|---|---|---|---|
|**Panel A: Basis**|||||
|Chengdu*After|-0.047**|-0.053***|-0.024*|-0.024**|
||(0.023)|(0.012)|(0.014)|(0.010)|
|Observations|4,186|4,186|4,186|4,186|
|R-squared|0.010|0.272|0.004|0.220|
|**Panel B: Placebo**|||||
|Chengdu*After|0.048*|0.047***|0.007|0.015|
||(0.026)|(0.017)|(0.008)|(0.012)|
|Observations|2,095|2,095|2,095|2,095|
|R-squared|0.011|0.557|0.010|0.294|
|**Panel C Het. effects**|||||
|Chengdu*After|-0.044*|-0.047***|-0.030**|-0.044***|
||(0.026)|(0.011)|(0.015)|(0.012)|
|Chengdu*After*Time|0.061***|0.057***|0.021|0.029**|
||(0.010)|(0.011)|(0.016)|(0.013)|
|Observations|4,186|4,186|4,186|4,186|
|R-squared|0.016|0.277|0.005|0.226|
|Spatial fixed effects|Yes|No|Yes|No|

_Notes:_ Village fixed effects are included. Village time-variant characteristics are controlled for throughout but not reported. These include total area and population, share of the households with access to pension and health schemes as well as the amount of agricultural subsidy, distance to the nearest county road, surface of the village road, and indicator variables for primary schools and medical stations within the village. Time is z score of google travelling time from a village to the center of Chengdu ( _μ_ =1.43h, _σ_ =0.40h). Standard errors adjusted for spatial correlation and serial correlation are in parentheses. If spatial fixed effects are included, otherwise they are adjusted for serial correlation within the village. *** p<0.01, ** p<0.05, * p<0.1.

19
# Table 1: Sources of Weather Data

|Data set|Length of record|Resolution|Time step|Data|Units|
|---|---|---|---|---|---|
|**Precipitation**||||||
|-Africa Rainfall Climatology version 2 (ARC2)|1983-current|0.1 deg|daily|total precip|mm|
|-Climate Hazards group InfraRed Precipitation with Station<br>data (CHIRPS)|1981-current|0.05 deg|daily|total precip|mm|
|-CPC Global Unifed Gauge-Based Analysis of Daily Precipita-<br>tion|1979-current|0.5 deg|daily|total precip|mm|
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts<br>(ECMWF) ERA5|1979-current|0.28 deg|hourly|total precip|m|
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) Surface Flux Diagnostics|1980-current|0.625x0.5 deg|hourly|rain rate|kg m<sup>2 </sup>s<sup>1</sup>|
|-Tropical Applications of Meteorology using SATellite data and<br>ground-based observations (TAMSAT)|1983-current|0.0375 deg|daily|total precip|mm|
|**Temperature**||||||
|-CPC Global Unifed Gauge-Based Analysis of Daily Tempera-<br>|1979-current|0.5 deg|daily|min, max temp|C|
|ture<br>57||||||
|-European<br>Centre<br>for<br>Medium-Range<br>Weather<br>Forecasts<br>(ECMWF) ERA5|1979-current|0.28 deg|hourly|mean temp|K|
|-Modern-Era Retrospective analysis for Research and Applica-<br>tions, version 2 (MERRA-2) statD|1980-current|0.625x0.5 deg|daily|mean temp|K|

_Note_ : The table summarizes the remote sensing sources and related details for precipitation and temperature data.