rafmacalaba/datause-encoder-data huggingface dataset:
variation, truck size is a significant factor: larger trucks reduce per unit transport price. As trucking companies possess bigger trucks and thus enjoy some economies of scale, they can charge less than individual truckers. And empty haul increases transport prices, as truckers may charge (part of) returning costs to customers.

23. The main difference between the results for the complete samples and for domestic routes (column 2) is that empty back hauls are not important for transport price setting on domestic routes – considering that average distances are only 84 km on domestic routes compared to over 2000 km on international ones. Column 3 provides an additional specification with the full sample where a dummy variable for domestic truckers is introduced. Controlling for truck size and trip distance, domestic truckers charge higher prices relative to those servicing international routes.

**Table 5: Determinants of trucking prices**

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent var<br>|iable: ln(price charg<br>Estimation: OLS<br>|ed togoper ton)<br> <br>||
|Sample|Full sample|Domestic truckers|Full sample|
|ln(one tripdistance)|0.668***|0.986***|1.023***|
||(0.017)|(0.094)|(0.021)|
|ln(average tare weight of|-1.092***|-0.711**|-0.428|
|vehicles)|(0.063)|(0.302)|(0.281)|
|ratio of mileage on empty-|2.354*|0.914|1.028**|
|Haul|(0.555)|(0.868)|(0.120)|
|dummyfor domestic|||2.417**|
|truckers (type 1)|||(0.455)|
|Constant|Yes|yes|Yes|
|Observations|59|31|59|
|R<sup>2</sup>|0.710|0.813|0.868|

- Note: 1. Clustered (by route type) robust standard errors in parentheses

2. * significant at 10%; ** significant at 5%; *** significant at 1%

# **ECONOMIC IMPLICATIONS OF INFRASTRUCTURE SHORTFALLS AND HIGH TRANSPORT COSTS**

## **Agriculture development**

24. In the agriculture sector, the choice of where and what to produce depends on natural endowments such as climate, rainfall, soil quality, water availability, as well as proximity to markets. Data from the Integrated Household Survey of 2004 (IHS2) are used to examine the spatial distribution of agriculture production in Malawi. The IHS2 data identifies households in over 564 Enumeration Areas across the country. By spatially linking these household data to geo referenced data on agronomic potential and proximity to markets, it becomes possible to assess how the choice and intensity of crop production varies with natural and economic geography.

### <u>Agronomic potential</u>

25. Agronomic potential, or potential (maximum) crop production, reflects the geo-climatic constraints on the quantity and quality of suitable land. We focus on the agronomic potential of three cash crops (coffee, cotton, and groundnuts), which share similar biophysical

- 11 -
|**Table 11(d): Hypotheses Tests for the A**<br>rm2|**lpha Vect**<br>Zero|**or: Weak**<br>Chi^2(1)|**Exogeneity and Joint Tests**<br>14.854 [0.0001]**|
|---|---|---|---|
|rgdp|Zero|Chi^2(1)|19.511 [0.0000]**|
|Interestrt|Zero|Chi^2(1)|1.9441 [0.1632]|
|DISC|Zero|Chi^2(1)|14.246 [0.0002]**|
|Dexrtend|Zero|Chi^2(1)|1.6468 [0.1994]|
|INFL|Zero|Chi^2(1)|3.2193 [0.0728]|
|Joint Significance for alphas|Zero|Chi^2(5)|56.676 [0.0000]**|
|Joint Significance for alphas and betas|Zero|Chi^2(12)|67.720 [0.0000]**|
|Joint Significance for alphas of (Interestrt),<br>(Dexrtend), (INFL)|Zero|Chi^2(3)|5.6872 [0.1279]|
|Joint Significance for alphas of (Interestrt),<br>(Dexrtend), (INFL) and beta of (Constant)|Zero|Chi^2(4)|7.6792 [0.1041]|
|**Joint Significance for alphas of (Interestrt),**<br>**(Dexrtend), (INFL) and beta of (Constant) and**<br>**FFUNDSRT**|**Zero**|**Chi^2(5)**|**10.497 [0.0623]**|
|Joint Significance for alphas of (Interestrt),||||
|(Dexrtend), (INFL) (rm2) and beta of (Constant) and<br>FFUNDSRT|Zero|Chi^2(6)|27.040 [0.0001]**|

P-values are in brackets.

- *** Significance level at 1%; , ** Significance level at 5%; *  Significance level at 10%

69
- Ditlmann, Ruth and Cyrus Samii (2016). “Can Intergroup Contact Affect Ingroup Dynamics? Insights from a Field Study with Jewish and Arab-Palestinian Youth in Israel.” _Peace and Conflict: Journal of Peace Psychology_ 22(4): 380

- Fajth, Veronika, Özge Bilgili, Craig Loschmann and Melissa Siegel, M. (2019). “How Do Refugees Affect Social Life in Host Communities? The Case of Congolese Refugees in Rwanda.” _Comparative Migration Studies 7_ (1), 1-21.

Falk, Armin and Nora Szech (2013). “Morals and Markets”. _Science_ 340(6133), 707-711.

- Ferguson, Neil, Eleonora Nillesen and Tilman Brück (2019): “Can Employment Build Peace? A Pseudo-Meta-Analysis of Employment Programs in Africa.” _Economics Letters_ 180: 99-101

- Fetzer, Thiemo (2020). “Can Workfare Programs Moderate Conflict? Evidence from India.” _Journal of the European Economic Association_ 18(6), 3337-3375.

- Ghreiz, Eyas (2020). “The Syrian Refugee Crisis in Jordan: Challenges and Future Opportunities for NGOs.” In _Syrian Crisis, Syrian Refugees_ (pp. 119-137). Palgrave Pivot, Cham.

- Grosch, Kerstin, Marcela Ibanez and Angelino Viceisza (2017). “Competition and Prosociality: A Field Experiment in Ghana.” GlobalFood Discussion Paper 113.

- Gubler, Joshua (2013). “When Humanizing the Enemy Fails: The Role of Dissonance and Justification in Intergroup Conflict.” In: _Annual Meeting of the American Political Science Association._

Hamadi, Ghadir (2021). <u>https://en.annahar.com/article/1004952-unemployment-theparalysis-of-lebanese-youth. Last retrieved: 19 September 2021.</u>

- Hangartner, Dominik, Elias Dinas, Moritz Marbach, Konstantinos Matakos and Dimitrios Xefteris, D. (2019). “Does Exposure to the Refugee Crisis Make Natives More Hostile?” _American Political Science Review_ 113(2):442–455.

- Harari, Mariaflavia and Eliana La Ferrara (2018). “Conflict, Climate, and Cells: A Disaggregated Analysis.” _Review of Economics and Statistics_ 100(4), 594-608.

- Hendrix, Cullen and Idean Salehyan (2012). “Climate Change, Rainfall, and Social Conflict in Africa.” _Journal of Peace Research_ 49(1), 35-50.

- Herriot, Peter and William Scott–Jackson (2002). “Globalization, Social Identities and Employment.” _British Journal of Management_ 13(3), 249-257

- Hugh-Jones, David and Martin Leroch (2017). “Intergroup Revenge: A Laboratory Experiment. _Homo Oeconomicus_ 34 (2-3), 117–135.

- Kheireddine, Bassem, Ana Maria Soares, and Ricardo Rodrigues (2021). “Understanding (In) tolerance Between Hosts and Refugees in Lebanon.” _Journal of Refugee Studies_ 34(1), 397-421.

27
# **Figure 2. Comparison of different equivalence scales**

<!-- Start of picture text --> Adult equivalent weights Child equivalence weight (first child) Child equivalence weights (second child)<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2 3 4 5 1 2 3 4 5 2 3 4<br>Number of adults Number of adults Number of adults<br>Per capita Square-root RUS Poverty Line Per capita Square-root RUS Poverty Line Per capita Square-root RUS Poverty Line<br>OECD modified BUC OECD modified BUC OECD modified BUC<br>Weight given to additional member (%)<br><!-- End of picture text --> 33
-45-

(ii) What Happens to Growth if Foreign Financing is Cut Back?

The results are dramatically different if a cutback in the current account deficit is imposed. This alternative assumes a zero current account deficit throughout the simulation period.<sup>18</sup> The internal adjustment is brought about by a matching cut in public sector itnvestment. The impact on output growth is severe: by 1992, the growth rate has fallen by two full percentage points. Output growth falls by 1.5 percentage points on average over the five year period.

Both private savings and investment fall under the impact of slower growth, but savings by much more than investment: the latter declines by 0.5 percent of GNP at the end of the simulation period, while private saving falls by 1.8 percentage points of GNP. Net private savings therefore declines by 1.3 percent of GNP over the simulation period. This has further adverse effects on fiscal policy: to still maintain external balance, a further round of fiscal cutbacks is needed. By the end of the period, fiscal deficits need to be cut back by 4 percentage points of GNP instead of just 2.5, the initial current account cutback. This is a vicious circle many high-debt countries that follow such orthodox policies encounter. Fiscal retrenchment to achieve .x'ernal balance causes a slump at home, which necessitates more of the same measures that triggered the slump to begin with. By the time this destabilizing process has worked itself out, output growth has declined a full 2 percentage points per year.

> I2/ The current account deficit concept used includes caly real interest payments; a zero current account deficit thus corresponds to a deficit at positive world inflation rates and positive foreign debt. World inflation is the rate of change in the dollar-based foreign price index used in the real exchange rate calculations.

ChhibberDisk/chhibb*r-Turkey Papcr/ac :07-28-88: c
also be attributed to the pattern of output changes and second best effects from factor productivity differences by sector.

**Table 7: Welfare Impacts with Tax Reform from Revenue Recycling** Equivalent <u>Variation, percent change from Baseline Household Expenditure</u>

|**Household**<br>**Income Deciles**|**Base**<br>**value**|**tax on**<br>**carbon**<br>**emission**<br>**by**<br>**activities**|**sales**<br>**tax on**<br>**energy**|**sales tax**<br>**on**<br>**energy-**<br>**intensive**<br>**sectors**|**tax on**<br>**carbon**<br>**emission**<br>**by**<br>**activities**|**sales**<br>**tax on**<br>**energy**|<br>**sales tax**<br>**on**<br>**energy-**<br>**intensive**<br>**sectors**|
|---|---|---|---|---|---|---|---|
|||**I. Referenc**|**e case**||**II. Rigid c**|**ase**||
|1st (poorest)<br>|11.22|-1.09|1.06|2.83|-1.56|3.42|2.88|
|2<sup>nd</sup><br>|15.76|-0.91|1.51|1.28|-1.35|3.88|1.09|
|3<sup>rd</sup>|21.79|-0.31|2.15|0.40|-0.18|5.47|0.06|
|4<sup>th</sup><br>|28.41|-0.26|2.05|-1.78|-0.19|4.96|-2.57|
|5<sup>th</sup><br>|36.81|-0.04|1.99|-2.48|0.29|5.17|-3.40|
|6<sup>th</sup><br>|47.1|-0.01|1.62|-3.43|0.34|4.51|-4.57|
|7<sup>th</sup>|65.6|0.01|0.94|-3.95|0.36|3.21|-5.24|
|8<sup>th</sup><br>|92.22|-0.16|-0.18|-4.76|0.03|0.97|-6.31|
|9<sup>th</sup>|135.95|-0.17|-1.16|-3.50|0.02|-0.74|-4.83|
|10th - lower 5%|105.23|-0.12|-0.95|-2.89|0.09|-0.37|-4.19|
|10th - next 1.25%|36.12|0.30|-1.11|-1.83|0.97|-0.23|-2.86|
|10th - next 1.25%|42.62|-0.13|-1.75|-4.16|-0.02|-2.24|-5.82|
|10th - next 1.25%|48.22|-0.30|-1.83|-5.26|-0.42|-2.75|-7.21|
|10th – top 1.25%|99.27|-1.10|-3.21|-9.92|-2.39|-7.27|-13.19|
|TOTAL|786.32|-0.27|-0.56|-4.07|-0.26|-0.05|-5.59|
|Adjustment to othertaxes||-0.25|-0.70|-3.07|-0.44|-1.35|-3.74|

_Source:_ CGE model simulations. _Notes_ : Energy = coal, electricity & gas, and petroleum; energy intensive sectors = basic iron and steel, transportation, basic non-ferrous metals, and metal products excluding machinery.

In all scenarios, capital is activity specific in coal, gold, and other mining.

Under revenue recycling, there is an equiproportionate change in the tax rates for all indirect taxes (production taxes, sales taxes, value added taxes, and import tariffs) that are not part of the policy experiments. Also, no revenue is recycled back to reduce base taxes on energy inputs or energy intensive sectors (instead those rates are held at base values, unless they are increased in the policy experiment).

Although the equity impacts follow the pattern of the previous results in the Table 5, there is further improvement for a sales tax on energy and a sales tax on energy intensive sectors. The reduction in other tax rates due to revenue recycling will generally benefit the poor. In the case of the sales tax on energy, welfare improvement is noted up to the 7<sup>th</sup> income deciles for the rigid case, thus in fact registering strong double dividends for most income groups except the top deciles.

The one clear exception is the sales tax on energy-intensive sectors, where the aggregate welfare losses are higher instead of lower when compared to Table 5. In this 22
# Table 4: Regression of municipal school introduction on lagged municipal birth trends

|_Outcome: New secondary school in_|_dicator_||||
|---|---|---|---|---|
|**A**|(1)|(2)|(3)|(4)|
|Births per 100 15–19 ears_(t-1)_|0.0012|0.0002|-0.0004|0.0029|
|, y|(0.0130)|(0.0130)|(0.0396)|(0.0490)|
|Observations|43,955|43,649|22,578|17,963|
|Municipalities|4,884|4,850|2,509|1,996|
|**B**|||||
|Births per 100 at age 15–19, 5-|0.0008|0.0008|-0.0010|-0.0018|
|year growth rate|(0.0012)|(0.0012)|(0.0018)|(0.0025)|
|Observations|43,952|43,646|22,578|17,963|
|Municipalities|4,884|4,850|2,509|1,996|
|||||Pop 10k–100k +|
|Municipalities in sample|All|Pop <500k|Pop 10k–100k|new school<br>1997–2005|

Notes: This table reports the results of regressing a binary variable, indicating a new school introduced to a municipality in year , on the number of births in period (Panel A), and the growth rate in teen births over the previous 4 years (Panel B). Coefficients are multiplied by 100 for display purposes. Growth in teen births calculated as the average annual percent change in cohort births between tt tt−1 _t_ and _t-4_ . Estimates condition on lagged primary school enrolment, nursery and preschool classrooms, municipality expenditures, municipality and year fixed effects. Municipality-clustered standard errors are reported in parenthesis.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

34
economy is relatively more volatile than other economies, the variance of the average sector-level growth rate across several years is the statistic of import—a high variance means high volatility.

A box and whisker plot (Figure 4) is a graphical depiction that allows the reader to visually determine whether the average annual industrial growth at the sector level in Russia indicates higher variances across time than that of other economies. The vertical line inside the grey box represents the median growth for each country between 1993 and 2009. The right and left boundaries of the grey rectangles represent the middle half of the data; they define the 25<sup>th</sup> percentile to the 75<sup>th</sup> percentile of annual rate of sector-level industrial output growth per <!-- Start of picture text --> Figure 4: The annual growth in output of Russian sectors exhibit<br>relatively higher variances—more volatility.<br><!-- End of picture text -->

**Figure 4: The annual growth in output of Russian sectors exhibit relatively higher variances—more volatility.**

<!-- Start of picture text --> Yearly growth of sector output (1993-2009)<br>OECD<br>Resource rich<br>Brazil<br>Russia<br>India<br>China<br>Korea<br>-1 -.5 0 .5 1<br>Data source: Authors' calculation from UNIDO 2011 Industrial Output Data (4-digit ISIC)<br><!-- End of picture text --> economy or group of economies. The lines or whiskers, outside of these boxes, delineate the most extreme values.<sup>14</sup> As can be verified, both the grey rectangles and the whiskers in the figure are markedly more extended for Russia than any other comparator. This means that the variance of average annual industrial growth in Russia is statistically larger than that of other economies, meaning that Russian sectorlevel growth has higher variances and is more volatile.

Having established that the variance of average annual industrial growth, for the period of time examined here, is higher than that of comparator economies, the next question is whether this volatility is the result of fluctuations in annual growth between sectors or between years. In other words, is the variance of annual growth explained by fluctuations in the growth of some sectors that in certain years grow fast then slow or is it that all sectors, year by year, generally grow fast or slow?

This is an important question because it may point to spillover or to macro-economic drivers of volatility. In other words, if fluctuations are explained by year or temporal fluctuations, where generally all sectors are in slumps or surges at the same time, that may indicate that these

> 14 Inter-Quartile Range (IQR) = x[75] – x[25] Highest Value <= x[75] + 1.5*IQR Lowest Value => x[25] – 1.5*IQR

8
# **Figure 2: Dynamics of normalized growth on the technology ladder**

**PANEL I**

<!-- Start of picture text --> 45 o line<br>Aggregate I<br>output<br>A<br>Y A t0<br>Global<br>technology<br>δ I t0 frontier at time t0<br>Technology<br>Income gap B gap<br>Y B t0 δ T t0<br>T B t0 T A t0 T<br>Technology endowment path<br><!-- End of picture text --> <!-- Start of picture text --> PANEL II<br>45 o line<br>Aggregate I<br>output<br>A<br>Y A tn Global<br>technology<br>Y A t0 frontier at time tn<br>δ I tn Δtδ A t (T)<br>Technology<br>Income gap B<br>Y B tn δ T tn jump<br>T B tn T A tn T<br>Technology endowment path<br><!-- End of picture text --> The PCA results are summarized in Table 1 in the Annex. Figure 3 plots the data with overlayed eigenvectors. The Figure plots on the same scale graphs of eigenvectors associated with the whole sample comprising of advanced economies (OECD countries) and Sub-Saharan African countries. In the first panel, the top figure does not include South Africa and Mauritius which have relatively more developed technological and scientific infrastructures, while the second Panel includes these two countries.

13
# **Living Arrangements and the Well-Being of the Elderly**

In terms of understanding the well-being of the elderly in developing countries, much prior research has relied on indirect evidence suggested either by patterns of living arrangements or by labor supply and retirement decisions. This focus derives from the fact that large sample surveys typically lack direct measures related to the provision o support within the household, including financial support, in-kind transfers and instrumental care. Living arrangements, or co-residence, are thus used as a proxy for elderly access to these frequently unobserved sources of support. Observed declines in co-residence with adult children have been cited most frequently as reasons for concern that China’s rural elderly are losing support from their adult children. Benjamin et al (2000) note that in rural north China over 85 percent of the elderly lived in extended households in 1935, but that this figure had dropped to just over 60 percent by 1995.<sup>5</sup> The decline in coresidence with adult children is strikingly evident over the six rounds, from 1991 to 2006, of the China Health and Nutrition Survey (CHNS). Figure 1 shows that in the CHNS, nearly 70 percent of adults over 60 in rural areas lived with an adult child in 1991, but by 2006 this share had fallen to just over 40 percent.

The change in living arrangements over both the long-term (from the 1930s to the 1990s as noted in Benjamin et al (2000)) and the short-run (from 1991 to 2006 in the CHNS) does not necessarily reflect a drop in the provision of care to the elderly. In-kind transfers, such as supply of labor on extended family plots and provision of care, are difficult to pick up in surveys, yet such transfers often occur both within and across households. Changes in living arrangements may reflect, in part, the increasing wealth of families. With increasing resources, co-residence may not be necessary to care for the elderly. Within villages in rural areas, elders and adult

> 5Selden (1993) concludes that a transition to the nuclear family imposes a heavy price on the rural elderly. Living arrangements are thought to be important for elderly support across East Asia, including Cambodia (Zimmer and Kim, 2002), Thailand (Knodel and Chayovan, 1997), and Viet Nam (Anh et al, 1997).

8
To test what determines TFP, we tried variables for last year’s spending on R&D (lnRn2, in form; for those spending nothing, we gave it a zero), capacity utility rate (lnCapUtl, ln form), dummy variable for adoption of foreign technology (ForgTech), top management’s years of education and experience prior to the appointment (lnmanage, in ln form) and percentage of products for export (lnexport, in ln form). We also control for the country average level subjective perception on the obstacle level in the investment climate – in terms of corruption and instability (lcorupM), financial or tax regulation (lfinanceM), and infrastructure or labor availability (lhardwareM) – they are coded in such a way that the higher the value, the worse the surveyed firm felt about that particular issue (see Annex 2 for details of coding).

**Table 7:  Estimates of determinants of firm level TFP, 2002-05**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|lnRnD2|0.02**|0.02**|0.02**|0.02**|
||(2.27)|(2.23)|(1.96)|(1.97)|
|lnCapUtl|0.45***|0.47***|0.46***|0.44***|
||(6.72)|(9.34)|(9.18)|(18.98)|
|ForgTech|0.19**|0.18**|0.17**|0.17**|
||(2.23)|(2.10)|(1.99)|(2.00)|
|Lnmanage|0.05||||
||(0.80)||||
|Lnexport|-0.03||||
||(-1.34)||||
|lcorupM|-0.86***|-0.86***|-0.37***|-0.42***|
||(-4.10)|(-4.18)|(-2.74)|(-8.82)|
|lfinanceM|0.64***|0.61***|||
||(3.06)|(2.99)|||
|lhardwareM|-0.30|-0.25|-0.10||
||(-1.12)|(-0.93)|(-0.38)||
|Observations|2391|2433|2433|2433|
|R-squared|0.41|0.41|0.41|0.41|

The results show that R&D spending, capacity utilization rate, foreign technology, corruption and instability index, and financial and tax regulation index are the statistically significant determinants of TFP. Surprisingly, the quality of management fails to be significant determinant of productivity, but this could be that managers’ decisions are involved in research and development, capacity utilization rate, and adoption of foreign technology. Export‐orientation of the firm does not appear to be a significant factor in productivity growth.

17
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 3. Railway indicators for Malawi and select countries, 2000–05**

||Kenya (KRC)|South Africa<br>(SPOORNET)|Malawi (CEAR)|Tanzania (TRC)|Tanzania-Zambia<br>(TAZARA)|Uganda (URC)|Zambia (RSZ)|
|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state run (0)|0|0|1|0|0|0|1|
|Traffic density, freight, 1,000 tonne-km/km|690|5,319|112|510|460|815|379|
|**Efficiency**||||||||
|Staff: 1,000 unit tariff (UT) per staff|185|3,037|204|228|300|181|452|
|Coaches: 1,000 passenger-km per coach|1,015|596|1,285|3,157|3,120|n.a.|2,772|
|Cars: 1,000 tonne-km per wagon|200|925|212|692|502|166|180|
|Locomotive availability in %|44.8|—|89.9|74.2|25.2|69.5|31.2|
|**Tariffs**||||||||
|Average UT, freight, U.S. cents/tonne-km|3.8|—|5.8|4.0|3.0|15.2|3.9|
|Average UT, passenger, U.S. cents/passenger-km|0.6|—|1.0|1.6|1.1|2.3|0.8|

_Source:_ Bullock 2009. Derived from AICD railways database, http://www.infrastructureafrica.org/aicd/tools/data. Note: — = data not available; n.a. not applicable.

## **Challenges**

Notwithstanding some improvements made under the concession, the performance of the CEAR still does not compare favorably with networks in neighboring countries (table 3). Relatively high freight tariffs and slow transit times continue to limit the competitiveness of rail vis-à-vis road transport. Moreover, the line is very lightly used—even relative to others in southern Africa—further challenging financial sustainability.

Malawi’s rail links with the rest of the continent are not functioning very effectively. There is interest in improving the performance of the Nacala corridor, which is Malawi’s shortest route to the sea through northern Mozambique, and which is currently beset with performance problems. Another proposal exists to extend the rail network northwards to connect with the Tanzania and Zambia Railway Authority (TAZARA) network; however, the associated costs are high ($200 million) and the economic case is not clear.

# Air transport

## **Challenges**

In common with other small countries in Africa, Malawi has a relatively small air transport market. The domestic market amounts to no more than 167,000 seats, while the international market comes to 435,000 seats. The intercontinental market is negligible. Nevertheless, relative to the population of the country the number of seats per capita—at just over 23—is an order of magnitude higher than would be found in larger countries.

9