rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Transport Services in Southern Africa and Lessons for Landlocked Developing Countries**

Regulatory regimes and efficiency of logistics services in Southern Africa are the most advanced in Africa. While Zambia is among the most distant landlocked countries from major ports such as Durban – the preferred port of entry in the sub-region - it benefits from a high transport quality and low transport prices. Southern Africa combines liberalization with enforcement of rules affecting regional competitiveness of the trucking industry to prevent potential negative externalities of the sector.

**Figure 4 Comparative average transport prices and productivity in Africa**

||**Average**<br>**transport price**<br>**per ton-km**<br>**(in US cents)**|**Average yearly**<br>**mileage**<br>**(in kilometers)**|**Average truck**<br>**fleet**<br>**(in years)**|
|---|---|---|---|
|**Southern Africa (North-South**|4-5|100,000-144,000|5|
|**Corridor)**||||
|**West Africa (Lomé-**<br>**Ouagadougou)**|6-8|40,000-50,000|Over 12|
|**East Africa (Mombasa-**|8|100,000-144,000|7|
|**Kampala)**||||
|**Central Africa (Douala-Chad**|10-25|60,000-70,000|12|

Source: trucking surveys and interviews of trucking companies in 2007.

While organized companies in Southern Africa can optimize their truck utilization and have almost similar ratio as European haulers (8000-12000 kilometers per month), oversupply is frequent on the main international corridors in Central or West Africa and their utilization can be as low as 2000 kilometers per month. Contrary to Southern Africa, Central Africa combines low quality transport and high transport prices.

This case study illustrates the importance of regional liberalization to the efficiency of trucking companies. Zambia’s case underlines the potential benefits to landlocked countries of exposing national service providers to wider regional competition. Competition in trucking services contributes to lower transport tariffs and increases transport quality.

In particular, landlocked francophone countries offer potential for benefiting from liberalization. The francophone countries follow two main intertwined practices of a transit bilateral treaty, which defines quotas for the fleet of the coastal and landlocked country and an informal practice of a queuing system to allocate freight to transporters, the “tour de rôle”. As a result of the implementation of both, a fixed price is set by the institution in charge of allocating freight and 24
CPP REFERENCE PORTFOLIO As at March 31, 2013 <!-- Start of picture text --> 100 6556 Equity 30% 5% 100%6<br>a|<br>30 Pe<br>25 10% 35% Debt<br>nnnee.hEtC<iti‘ rh!’<br>‘Canadian Equities Global Equities Canadian Foreign Sovereign Total<br>Nominal Bonds Bonds (Hedged)<br><!-- End of picture text -->
# **4. Gender Inequality and Development Today**

With a few exceptions, most of the cross-country literature reviewed in section 2 uses data on gender inequality that date back at least 10 years. This lack of recent data has occurred because the topic has been at the heart of the policy debate for decades, so academic researchers have been accumulating evidence since the late 1980s. Another contributing factor is that cross-country growth regressions analyze economic growth over long periods of time, typically since the 1960s or 1970s, and gender inequality must be measured at the beginning of the period to study its role as a determinant of economic growth.

The 2012 World Development Report presents a detailed account of the state of gender inequality today. Strikingly, health and education gaps have substantially narrowed and, in most cases, have closed altogether. However, these improvements have not been accompanied by economic and political parity or equality. This finding suggests that different policies are needed to promote economic and political parity indicative of the process of development, at least as it has been observed in today’s richest nations, because previous gender-equality policies have not automatically produced this parity.<sup>5</sup> In this section, we provide evidence on the state of gender-education gaps that have been the focus of most of the macroeconomics literature. The main purpose of this section is to illustrate the sensitivity of cross-country estimates to the choice of theory-related specification and of the sample. Readers interested in exploring gaps in other dimensions are referred to the World Bank’s 2012 World Development Report.

Figures 1 and 2 review two measures of the education gap: the female/male literacy ratio and the female/male secondary-education attainment for people over the age of 25. Both measures indicate that gender inequality in education had almost disappeared as of 2010. The median female-male literacy ratio was 0.99; over half of the sample countries had achieved gender parity, and only a handful had ratios below 0.80. The median of the female-male secondary-education attainment ratio was 0.93, and the ratio was one to one or higher in over half the sample countries, indicating that women had higher attainment than men.

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Blume, Lorenz and Voigt, Stefan, 2005. “The Economic Effects of Human Rights”, University of Kassel, Working Paper No. 66/04 Burkhart R. E. and M. S. Lewis-Beck, 1994. Comparative democracy: the economic development thesis. American Political Sciences Review 88(4):903–910 Calderón C., 2000. “Causality and Feedback between Institutional Measures and Economic Growth.” Economics and Politics 12 (1):69–81.

Carlsson, F. and Lundström, S. 2002. Economic freedom and growth: Decomposing the effects. Public Choice 112: 335–344.

Chauffour, Jean-Pierre, 2009. “The Power of Freedom: Uniting Human Rights and Development”, Cato Publisher. Washington DC.

Cole, J. H. 2004. Economic freedom and the determinants of world economic growth, 1980–99. Paper presented at the Conference of the Association Private Enterprise Education (APEE).

de Haan, Jakob & Siermann, Clemens L J, 1998. " Further Evidence on the Relationship between Economic Freedom and Economic Growth," Public Choice, Springer, vol. 95(3-4), pages 363-80, de Haan, Jakob, and Jan-Egbert Sturm, 2000. “On the Relationship between Economic Freedom and Economic Growth”, European Journal of Political Economy Vol. 6, No. 2.

de Haan, Jakob, Sturm, Jan-Egbert, Leertouwer, Erik, 2002. Which Economic Freedoms Contribute to Growth? A Comment. Kyklos, Vol. 55, pp. 403-416.

De Haan J., S.Lundström and J. E. Sturm (2006), Market-oriented institutions and policies and economic growth: A critical survey, Journal of Economic Surveys, Blackwell Publishing, vol. 20(2), pages 157-191.

Dawson, J. W. 1998. Institutions, investment, and growth: new cross-country and panel data evidence. Economic Inquiry 36: 603–619.

Dawson, J. W. 2003. Causality in the freedom-growth relationship. European Journal of Political Economy (Special issue on Economic Freedom) 19: 479–495.

De Schweinitz 1959. Industrialization, labor controls, and democracy, Economic Development and Cultural Change 7: 385-404.

Durham, B. J., 1999. “Economic Growth and Political Regimes.” Journal of Economic Growth 4: 81–111.

Easterly, William, 2001. “The Elusive Quest for Growth: Economists’ Adventures and Misadventures in the Tropics” (Cambridge, Mass: MIT Press).

Faria H.J. and H.M. Montesinos 2009. Does economic freedom cause prosperity? An IV approach, Publich Choice (2009) 141, pp. 103-127.

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government owned banks in the borrower country, where a bank is considered government owned if 50 percent or more of the shares are controlled by the government. These two variables have means of 73.31% and 16.75%, respectively. In the bilateral regressions, we also control for geographic distance and common language between lenders and borrowers. Specifically, Log distance is calculated as the natural logarithm of the physical distance between the capital cities of the respective borrower and lender countries with a mean of 8.417, and Common spoken language is the probability that a pair of people chosen at random from the borrower and lender countries understand one another in some language with a mean of 0.305. The latter variable is taken from Melitz and Toubal (2014).

Going beyond loan volumes, we also consider the choice of the lead bank in an international loan syndicate. For this purpose, we define Lead to be a dummy variable indicating a lead arranger role for a bank in a loan syndicate. Following Bharath et al. (2011) and Berg et al. (2016), we set Lead equal to one if 1) the reported lender role in Dealscan is either “Admin agent”, “Agent”, “Arranger”, or “Lead bank”; or 2) the lead arranger credit field equals “Yes”; or 3) if the loan has a sole lender. Lead has a mean of 42.7%.

The lead bank regressions include regulatory and institutional variables for the lender country and several additional control variables for the parent lender bank. Log assets is the natural logarithm of the bank’s total assets lagged by one year, and Log syndicated lending is the log of 1 plus the sum of the dollar value of all loans provided by a lender in a given year minus the pertinent loan amount with means of 13.63 and 22.52, respectively.<sup>14</sup> Larger banks by assets may be in a better position to assume the lead bank role in a loan syndicate, as this tends to require a bank to retain a relatively large share of the overall facility on its own books (Sufi, 2007). Loans/deposits is the ratio of all loans to deposits lagged by one year with a mean of 0.902. Banks that retain more loans on their balance sheets may be more

> 14 <mark>Bank accounting data are taken from Compustat.</mark>

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10 Figure 3: Distribution of vulnerability in 1999, 2008, and 2009 among net oil importers <!-- Start of picture text --> 70<br>63<br>1999 2008 2009<br>60<br>50<br>38<br>40 34<br>31 30 31<br>30 26<br>20 17<br>12<br>9<br>10 5<br>1 0 2 1<br>0<br>0 to 2.5% 2.5 to 5% 5 to 10% 10 to 20% Above 20%<br>Vulnerability as percentage of GDP<br>Sources: Table A1.1 and author’s calculations.<br>Figure 4: Distribution of vulnerability in 1999, 2008, and 2009 among net oil exporters<br>40 37<br>1999 2008 2009 35 35 36<br>35 33 33<br>30<br>23<br>25<br>21<br>20 17<br>14<br>15 12<br>10<br>5<br>5<br>0<br>Below -30% -15 to -30% -15 to -5% -5 to 0%<br>Vulnerability as percentage of GDP<br>Percentage of countries<br>Percentage of countries<br><!-- End of picture text --> _Sources:_ Table A1.1 and author’s calculations.

Summary statistics by income and region are available in appendix 1, tables A1.2 and A1.3, respectively. The average vulnerability was the highest for low-income countries for all three years—in part because they had the highest concentration of non-oil producers—and the median was also the highest for low-income countries in 1999 and 2008. High-income countries had the lowest median vulnerability for all three years—reflecting the low oil intensity of their economies—and the lowest average vulnerability in 2008 and 2009. Upper-middle-income countries had the highest share of countries with falling vulnerability. The regional breakdown in this paper excludes high-income countries and focuses only on developing countries. The Middle East and North Africa, with some major oil exporters, was the only region with negative average and median vulnerability indices for all three years; Europe and Central Asia and Sub-Saharan Africa also had negative average vulnerability indices in every year. At the opposite end of the spectrum was South Asia, with no net oil exporters, which had the highest average vulnerability
9 Given that we use data from 43 countries, with five asset quintiles by country, we have a theoretical maximum of 215 observation. In practice, we have only 175 observations because of missing dependant variables.<sup>12</sup> We experimented with linear, semi-log and log forms. The linear and semi-log forms yielded similar results (as measured by the R<sup>2</sup> and statistically significant variables) to those presented here. We settled on the log forms for ease of interpretation (i.e., to interpret coefficients as elasticities). The regressions were estimated as Seemingly Unrelated Regressions using the Stata package.

# **4. Empirical Results**

# **_4.1. Baseline Results_**

Table 1 provides the baseline results from the estimation. Regressions 1-3 suggest that after controlling for other variables, national-level GDP per capita remains a strong determinant of health outcomes. The elasticity is largest for malnutrition, with a 10 percent increase in GDP per capita leading to a 2.5 percent reduction in the prevalence of malnutrition. National-level female illiteracy is also statistically significant for all three outcomes, with an estimated elasticity of 0.4 in each case. Thus, a 10 percent reduction in female illiteracy improves the health outcomes by 4 percent. National-level income inequality and fractionalization are statistically significant only for child mortality, while urbanization is statistically significant only for child malnutrition.

We now turn to variables at the quintile level. The asset quintile dummies are always statistically significant. For example, the estimates imply that infant mortality is 41 percent higher in the poorest asset quintile than in the richest (note that this variable is in semi-log form, while all other variables are in log form). Higher child malnutrition leads to higher infant and child mortality, with an elasticity varying between 0.21 and 0.30. Note that excluding the malnutrition variable from regressions 1-2 generated higher coefficients for GDP in those regressions, suggesting that part of the income effect on health outcomes works through the reduction in malnutrition documented in regression 3. The coefficient on the mother’s Body Mass Index (BMI) is strongly significant for child

> 12 Note that when explanatory variables are missing, the variable is set to zero and a dummy variable equal to 1 is included in that regression.
<!-- Start of picture text --> 0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Uzbekistan Tajikistan Latvia Estonia Armenia Mongolia Georgia Germany Lithuania Azerbaijan Slovenia Moldova Belarus Kyrgyzstan Czech Republic Kosovo Hungary Croatia Kazakhstan Montenegro Poland Russia Serbia Bulgaria Ukraine Slovak Republic Cyprus Greece Bosnia and Herzegovina Romania FYR Macedonia Italy Turkey Albania<br><!-- End of picture text --> _Source_ : Tiwari et al (2017) Similarly, downward mobility when mobility is defined over educational attainment also requires some additional qualification. Unlike income or earnings, lower educational attainment than the parental level of attainment may not always represent a slide in overall welfare or wellbeing. For example, for a child whose parents already hold the highest tertiary degree, the only two choices available are to either match that level or to attain a lower level. Even though the entire motivation for us to analyze intergenerational mobility using education is its correlation with economic/or labor market returns, this relationship may weaken for specific cases at the higher end of the education distribution. To take a concrete example, on average a PhD may deliver higher earnings than a BA in any labor market, but there may very well be specific cases where this does not hold. The fact that we use coarser definitions of education does mitigate this quite substantially, but we note this as something that needs to be taken into consideration while interpreting the results.

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An important aspect of the reform was that the amount of the subsidy was now limited to $8.50 per month to each eligible household regardless of how many bottles per month the household purchased. Those households who consumed more than one bottle per month would be worse off under the new scheme. This could potentially hurt some of the poor. However, a survey conducted in May 2011 indicated that one 25-lbs bottle of LPG was enough to cover their monthly consumption for 80 percent of households. Among the 20 percent of households for which one 25-lbs bottle was not enough to cover their monthly consumption needs, the majority (almost 70 percent) were households with a monthly income above the average. Finally, since the subsidy was for household consumption, not industrial use, indirect effects on the price level and other second-order effects can be thought of as relatively minor in this case.

To sum up, the incidence analysis suggests that the winners of the reform included the poor that did not use LPG but would now receive $8.50 per month and any household that consumed less than 25-lbs of LPG per month. The losers would be the top 6 percent households in electricity consumption,<sup>2</sup> the owners of more than one property, those households that consumed more than one 25-lbs bottle of LPG per month, as well as the smugglers and the distributors (who saw a decline in volumes). The overall incidence by income decile of the LPG subsidy before and after the April 2011 reform is shown in figure 1.

The incidence analysis is based on _de jure_ eligibility for the subsidy not on whether people actually received the subsidy. This is an important distinction because it quickly became apparent that a number of households that were entitled to the subsidy did not cash it in. In the estimates of the authorities around 70,000 households did not collect the subsidy even though they were legally entitled to it. While we do not have information on the income level of these households, the anecdotal evidence available suggests that these were households with access to electricity and relatively high income. A common explanation for this surprising behavior is that they did not think it was fair to claim a benefit that was meant for the poor. It is also possible that the benefit was lower that the opportunity cost of cashing it (going to the bank, queing, etc..). As a result, the _de jure_ incidence analysis may underestimate the pro-poor nature of the reform. At the same time it is also possible that some poor households were not well informed about the benefit or were unable to prove their eligibility which would have reduced the pro-poor nature of the reform. However, there

> 2Meaning with an electricity consumption higher than 200kw/h.

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through two stress tests, described below. These stress tests do not include any feedback effect on growth and the primary deficit. Their outcome can be considered a lower bound of possible realizations, which would be amplified by lower real growth and a higher primary balance, resulting, for example, from lower revenue.

A scenario analysis which increases the real cost of financing by two percentage points for all countries results in two countries increasing their debt-to GDP ratio above 50 percent by 2030. Figure 17 introduces an external permanent increase to the real interest rates by 2 percentage points compared to their historic average. In this scenario, all but three countries in our sample still maintain a negative real interest-growth differential. While higher interest rates would lead to a deterioration in debt dynamics in all countries, Cambodia and Bangladesh, among LDICs, and Albania, Armenia and Georgia, among EMs are projected to face an increase of public debt to GDP ratio, which would increase above 50 percent of GDP in Albania and Armenia and would remain narrowly below the 50 percent level for Georgia.

Figure 17. Scenario 1: Interest Rates are Two Percentage Points Higher (Change in debt-to- GDP ratio, 2016-30) <!-- Start of picture text --> LIDCs EMs<br>3% 36% 40% 120%<br>2% 35%<br>30% 100%<br>1% 34%<br>0%<br>33% 20% 80%<br>-1%<br>32%<br>-2% 10% 60%<br>31%<br>-3%<br>30% 0% 40%<br>-4%<br>-5% 29% 50% line<br>-10% 20%<br>-6% 28%<br>-7% 27% -20% 0%<br>KHM BGD ALB ARM GEO<br>Baseline BRI Debt to GDP ratio including BRI Baseline BRI Debt to GDP ratio including BRI<br>In percent of GDP In percent of GDP<br>Debt to GDP at end-2030 Debt to GDP at end-2030<br><!-- End of picture text --> Sources: WEO, WIND Database, LIC DSF DSAs and de Sources: WEO, WIND Database, LIC DSF DSAs and de Soyres (2019). Soyres (2019).

In a second scenario, we have hardened the budget constraint of some countries by setting real interest rates to be the maximum between one percent and the value under the baseline (Figure 18). Under this scenario, four countries would accumulate a debt-to-GDP ratios above 50 percent by 2030 as a result of BRI. Also in this scenario all but three countries would face a negative interest rate growth differential, but this differential would reduce from a median of -7 percent in the base case to a median of -3 percent in this scenario.<sup>47</sup> This increase would be consistent with a number of plausible outcomes that could be realized in the medium and long-term: (i) normalization of monetary policy in advanced economies; (ii) 47 The authors also developed a stress test which would assume that the real interest rate would be not smaller than baseline growth, implying that all countries would face a positive interest-growth differential in a no-BRI scenario. Under this scenario a total of six countries would end up with a debt to GDP ratio that would increase above 50 percent by 2030. However, this scenario would be too extreme, as many countries would face an increase of real interest rates of more than 10 percentage points compared to the base case.

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# **IV. Concluding Remarks**

In this paper, we investigate the potential impact of selection mechanisms in raising plant‐level productivity in Ethiopia. Specifically, we are interested in how firms’ entry and exit contribute to the pace of factor reallocation and TFP growth within industries—and whether these processes are accelerated in larger cities. We carry out this analysis using data from the Ethiopian census on manufacturing firms which covers the period 2000 to 2010. Importantly, these data include information on plants’ physical outputs and their prices which allow us to distinguish between revenue‐based measures of total factor productivity (TFPR) and those based on physical productivity (TFPQ). Our analysis reveals that these two measures generate very different results, suggesting that physical productivity measures (TFPQ) are better suited to examining firm dynamics when local producers have some degree of market power. In addition, we find evidence that less efficient (higher cost) firms are more likely to exit than their more efficient (lower cost) rivals—but only when we control for producers’ transport costs. In urban areas, firms with lower transport costs are more likely to exit, suggesting that such firms are shielded to some extent from competition, potentially slowing down the process of industrialization.

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