rafmacalaba/datause-encoder-data huggingface dataset:
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Benchmarking port indicators: Freetown as compared with selected other ports**<sup>*****</sup>

||Freetown,<br>Sierra<br>Leone|Monrovia,<br>Liberia|Cotonou,<br>Benin|Abidjan,<br>Côte<br>d'Ivoire|Tema,<br>Ghana|Apapa,<br>Nigeria|Harcourt,<br>Nigeria|Dakar,<br>Senegal|Lomé,<br>Togo|
|---|---|---|---|---|---|---|---|---|---|
|_Capacity_**:**||||||||||
|Actual container handled<br>(TEU/year)|45,000|50,000|158,201|500,119|420,000|336,308|7,900|331,191|460,000|
|General cargo handling<br>capacity (tons/year)|7,000,000|700,000|2,500,000||8,500,000|5,000,000|2,000,000|||
|_Efficiency_**:**||||||||||
|Container dwell time -<br>average (days)|15|15|12|12|25|42||7|13|
|Truck processing time for<br>receipt and delivery of cargo<br>(turn-round time) - average<br>(hours)|5|5.5|6.0|2.5|8.0|6.0||5.0|4.0|
|General cargo vessel pre-<br>berth waiting time - average<br>(hours)|48|3|48|3|9.6|36|38|24||
|General cargo vessel stay<br>(turnaround time) - average<br>(hours)|12|3|48|2|48|41|46|60||
|Container crane<br>productivity* (container per<br>hour)||||18|13|12||||
|General cargo crane/gang<br>productivity - average (tones<br>per hour)|12|16|15|16|14|9|8||23|
|_Tariffs_**:**||||||||||
|Average general cargo<br>handling charge, ship to<br>gate (US$/tonne)|NA|200|180|260|168|155||160|220|
|Average dry bulk handling<br>charge, ship to gate or rail<br>(US$/tonne)|5.5|10.5|8.5|13.5|10|8|8|15|9|
|Average liquid bulk handling<br>charge (US$/tonne)|3.3|4|5|5|3|||5|5|

* Data for the Freeport of Monrovia relate to 2008, but no data were available for the Port of Buchanan. Source: Ocean Shipping Consultants 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

# Air transport

## **Achievements**

Sierra Leone has shown steady growth in all segments of the air-transport market (figure 12). Continued growth from 2001 to 2009 defies the trend for the rest of West Africa, which witnessed a collapse between 2001 and 2004 after the demise of Air Afrique and others. The most likely explanation is that Sierra Leone‘s capacity was depressed during the civil war, and therefore as services were reestablished there was no prior peak from which to decline. Air Afrique did not serve the country in 26
Figure 4: Historical weighted average producer loss ratio for major crops, 2000-2008 <!-- Start of picture text --> Chilly<br><!-- End of picture text --> Source: Data from AICI

# _Strengths of NAIS_

The NAIS has significant advantages as compared to both multiple peril crop insurance (MPCI) and weather indexed insurance. First, individual MPCI would have been prohibitively expensive, or even impossible on technical and administrative grounds, in a country such as India with so many small and marginal farms (Hazell, 1992). Further, the method of using an ‘area based approach’ has several other merits including, most importantly, the mitigation of moral hazard and adverse selection.

Second, the mismatch between claim payments and losses incurred, known as basis risk, from weather indexed insurance is usually considered to be higher than that from area yield index insurance (Carter et al., 2007). This is partly because area yield insurance can cover more perils than weather based insurance. Also, in an Indian context insurance unit size is typically smaller under NAIS than WBCIS, due to limited weather station infrastructure, leading to an increased ability of NAIS to cover localized perils.

Finally, by using the banking system both to collect farmer insurance premiums and to channel payments, the NAIS has low supply-side transaction costs. This low-cash and transaction point intensity, together with the ‘area based approach’, has enabled low leakages in the channeling of claims.

# _Challenges faced by NAIS_

However, despite NAIS insuring 25 million farmers, 95 million are not yet insured. Ignoring the 11% of farmers for whom purchase of NAIS cover is a compulsory precondition for taking out a loan for agricultural purposes, only 6% of farmers voluntarily purchase cover. This is particularly surprising given the large subsidies afforded to NAIS: for every 1 rupee of farmer premium paid between 2000 and 2008, NAIS disbursed 3.5 rupees in claim payments. The challenges faced by NAIS fall into the following seven categories.

7
Total factor productivity (TFP) growth is the residual of the actual GDP growth rates and the growth rates in physical and human capital, multiplied by their shares in national income. Results in Table 4 show that: 1) factor accumulation is the driver of growth in Egypt, similar to results of Kheir el Din and Moursi (2007) and Favaro et al. (2009); 2) physical capital accumulation is the main driving force in periods in which FDI is booming (early 1990s and late 2000s); and 3) TFP growth is positive during the reform periods, and negative in the policy reversal sub period. The results are worrying in terms of the prospects for growth, to the extent that capital accumulation is subject to decreasing marginal returns. This was the base of Krugman’s (1994) pessimism on the growth prospects of East Asian countries whose growth was also driven by capital accumulation.

International experience shows that productivity growth accounts for most of the difference between successful growth experiences and unsuccessful ones (Easterly and Levine, 2001). But it also shows that countries growth may be driven by factor accumulation initially, and then be driven by productivity growth such as in the US experience in the late 19<sup>th</sup> century and Japan during the early 20<sup>th</sup> century (Kim, 2001).

**Table 4 - Contribution to Growth by Factor Accumulation and TFP Growth**

|_Period average_|**1991-1998**|**1999-2004**|**2005-08**|
|---|---|---|---|
|||_percent_||
|Human Capital|0.22|3.18|2.32|
|Physical Capital|1.94|2.42|3.36|
|TFP|1.79|-1.49|0.7|
|**GDP growth rate**|**3.9**|**4.1**|**6.4**|

_Source: Authors’ calculations_ The rise in capital intensity, especially since 2001, is partly explained by the energy subsidies and the negative real interest rates. With the increase in energy prices worldwide, the economic value of the subsidy in Egypt tends to increase, giving domestic producers an unsustainable competitive edge. On the other hand, negative interest rates experienced in Egypt induce a low cost of capital, which explains the acceleration of investment described in the above section.

Evidence for Egypt shows that TFP falls when unsustainable policies are adopted and rises during periods of market-oriented reform. From Figure 9, it can be seen that TFP growth was negative until the 1990s, when it turned positive. After a brief reversal during the late 1990s and early 2000s, TFP growth turned positive again. This evolution matches closely the three sub periods in which we subdivided the analysis in the present paper. The positive productivity growth sub periods match those of market-oriented reforms. The recovery of TFP growth in the 1990s is particularly related to two factors. First, the trade policy reform reduced the simple average tariff from 42 percent in 1991 to 26 percent in 1998. The reform process stalled in the early 2000s: during 2000-2004, the trade13
<!-- Start of picture text --> 80 General Government Debt 80 200 Household Financial Leverageg 16<br>(Percent of disposable incor)<br>G====5 Debt / income<br>70-4<br>_ Euroats L 70 160 === interest<br>-_——-"- ~ * pmts /income (hs) 12<br>aid ae<br>Portugal so -# fa=o EeBE =Eel EG]= EberBS = =<br>50 50 ee a ee 4<br>40 7 ES ess]. EDs]<br>(Percent of GDP) = = =e<br>40 40 QO a5 == =) =e act i= 0<br>2000 2002 2004 2006 2008 02 o3 04 O85 O06 OF a8<br>Non-financial Corporations Non-financial Corporations<br>100 Leverage 30 8 Profit and Interest Coverage 12<br>% % chang (in percent)<br>90 [== debt to equity 1/ A \<br>20 ——debttoequity(ns)1/ 20<br>8<br>70 /]<br>60 | 10 4 ---""<br>| > ae<br>50 “S27 4<br>——-—- Retum on investment<br>| 0 ceeeeeem= Retum on equity<br>. i it —_—_<br>30 -10 0 a]<br>05 06 O7 08 03 2000 2002 2004 2006 2008<br>250 ALS= Feb 9 40 Net International Investment Position<br>(Percentof GDP) a 20<br>200 a<br>L 7 0 ke<br>‘ pu<br>150 / 6 20 KE<br>/ / 5 Be<br>aN f 40 Ff:<br>100 F§ +7” -60 Fe<br>3 fe<br>-80 Be<br>50 —— Extemaldebt 2 eelF (Percentof GDP, 2008)<br>-100<br>——— Inton e xtemalrestdebt (RHS) 1<br>0 0 -120 EFQOSZaatqgeZzaqaQgDAb<br>1998 eSe = Ww a<br>2000 2002 2004 2006 2008 ae aI ae s eFr2c = suas<br><!-- End of picture text -->

Sources: Bank of Portugal, Eurostat and staff calculations. 1/Non-consolidated figures.
(MMD) was elected. Faced with a profound recession, the new government implemented economy-wide reforms such as macroeconomic stabilization, exchange rate liberalization, trade and industrial reforms, and maize subsidies deregulation. More importantly for our purposes, privatization of agricultural marketing in cotton was also pursued.<sup>2</sup> Traditionally, the Zambian cotton sector was heavily regulated. From 1977 to 1994, cotton marketing was controlled by the Lint Company of Zambia (Lintco), a parastatal organization. Lintco set the sale prices of certified cotton seeds, pesticides, and sprayers, as well as the purchase price of cotton lint. Lintco had monopsony power in cotton purchases and monopoly power in inputs sales and credit loans to farmers.

In 1994, comprehensive cotton reforms began to take place. Most interventions were eliminated when Lintco was sold to Lonrho Cotton. Initially, a domestic monopsony developed early after liberalization. Soon, however, expanded market opportunities induced entry of private ginners such as Swarp Textiles and Clark Cotton. Because these three major firms segmented the market geographically, the initial phase of liberalization did not succeed in introducing competition, giving rise, instead, to geographical monopsonies rather than national oligopsonies.

At that moment, Lonrho and Clark Cotton developed an outgrower scheme with the Zambian farmers. In these outgrower programs, firms provided seeds and inputs on loans, together with extension services to improve productivity. The value of the loan was deducted from the sales of cotton seeds to the ginners at picking time. Prices paid for the harvest supposedly reflected international prices. Initially, repayment rates were high (around roughly 86 percent) and cotton production significantly increased. We called this the outgrower introductory phase.

By 1998, the expansion of cotton farming attracted new entrants, such as Amaka Holdings and Continental Textiles. Instead of the localized monopsonies, entrants and incumbents started competing in many districts. As competition among ginners ensued, an excess demand for cotton seeds developed. Several concurrent factors explain why, however, farmers could not realize the full benefits of the competition phase. First, some firms that were

> 2For more details on cotton reforms in Zambia, see Food Security Research Project (2000), and Cotton News (2002).

4
19

||**T**|**able 4. Sha**|**red Prosp**|**erity Regre**|**ssions**||||
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|
||F.E.|F.E.|F.E.|F.E.|System GMM|System GMM|System GMM|System GMM|
||S. Prosperity|S. Prosperity|S. Prosperity|S. Prosperity|<br>S. Prosperity|<br>S. Prosperity|<br>S. Prosperity|<br>S. Prosperity|
|Lagged Level ("convergence")|-0.424***|-0.430***|-0.492***|-0.566***|-0.053|-0.138**|-0.143*|-0.166**|
||[0.063]|[0.066]|[0.081]|[0.081]|[0.038]|[0.062]|[0.070]|[0.068]|
|∆(Cyclically Adj. Primary Surplus/GDP)t-1|0.464|0.438|0.443|0.470|1.641***|1.468***|1.425***|1.462***|
||[0.325]|[0.328]|[0.313]|[0.309]|[0.457]|[0.391]|[0.388]|[0.414]|
|(GDP Growth per capita)t-1|-0.272|-0.286|-0.284|-0.212|-0.324|-0.245|-0.293|-0.356|
||[0.197]|[0.198]|[0.191]|[0.204]|[0.287]|[0.272]|[0.256]|[0.285]|
|(Employment rate)t-1|0.026|0.061|0.060|-0.056|0.095|0.364|0.402|0.165|
||[0.210]|[0.243]|[0.227]|[0.242]|[0.677]|[0.591]|[0.588]|[0.542]|
|(Inflation)t-1|0.017|0.014|0.038|0.027|-0.083|-0.186|-0.156|-0.078|
||[0.056]|[0.055]|[0.051]|[0.051]|[0.231]|[0.168]|[0.147]|[0.190]|
|(Sub-national Social Expenditure)t-1||-0.396|-0.310|-0.315||-0.879*|-0.945*|-0.926*|
|||[0.304]|[0.284]|[0.271]||[0.448]|[0.490]|[0.456]|
|(Federal Social Transfers/GDP)t-1||1.379|0.824|-0.081||0.362|0.746|0.608|
|||[0.834]|[0.638]|[0.984]||[2.052]|[1.866]|[1.691]|
|(Years of Education)t-1|||0.185*|-0.102|||0.011|-0.083|
||||[0.108]|[0.068]|||[0.059]|[0.060]|
|(Returns to Education)t-1||||0.881***||||0.231|
|||||[0.222]||||[0.213]|
|Constant|0.000|0.000|0.000|0.000|0.294|0.691**|0.664**|0.967**|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.431]|[0.314]|[0.300]|[0.425]|
|Time Trend|No|No|No|No|No|No|No|No|
|Time Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Pesaran (2004) cross-sectional dependence test|-0.15|-0.26|-0.45|-0.65|-0.33|-0.44|-0.45|-0.60|
|Sargan test|||||127.8***|142.2**|149.1**|161.4***|
|Hansen test|||||3.728|1.977|1.639|1.167|
|Arellano-Bond AR(2) test|||||0.223|0.0408|-0.0356|-0.152|
|<br>Observations|405|405|405|405|405|405|405|405|
|Number of groups|27|27|27|27|27|27|27|27|
|<br>R-squared|0.441|0.444|0.459|0.482|||||
|HAC robust (GMM regressions) or Driscoll-Kraay (<br>is cross-sectional independece. Null of Arellano-Bo<br>restrictions.|F.E. regressions)<br>nd test is that firs|standard errors<br>t-differenced err|in brackets. ***<br>ors exhibit no 2|p<0.01, ** p<0.0<br>nd order serial co|5, * p<0.1. Null hyp<br>rrelation. Sargan a|othesis of Pesaran<br>nd Hansen tests of|(2004) test for regr<br>tests of the validity|ession residuals<br>of overidentifying|
24 liberalization on wage inequality in Brazil. The authors also show that this inequality‐reducing effect is almost entirely due to employment reallocation across industries (changes in _λ0_ ), rather than to changes in wage or skill premia.

The second approach goes into further detail in the modeling of the general equilibrium (or macroeconomic) relationships that link the behavioral responses of different firms and individuals to a particular shock or policy change, and to one another. Rather than relying on a reduced‐form relationship between a set of observed exogenous variables (such as changes in tariff rates) and a set of industry‐level shifters (as above), studies in this second approach estimate a full macroeconomic (or computable general equilibrium) model. This “macro model” (for short) is used to generate a set of “linkage aggregate variables”, such as vectors of wage rates and employment levels, for certain combinations of sectors and types of workers (e.g. wages and employment for high‐skilled workers in the informal sector in urban areas).

These variables are then used to connect the macro model to a set of earnings and occupation equations – similar to (24) and (25) above ‐ estimated on household (or labor force) micro‐data. A convergence algorithm is used to ensure that the counterfactual distributions of employment choices and wage rates add up to the aggregate simulations from the macro model. At the micro‐level, the simulation generates counterfactual occupation and income distributions ‐ and growth incidence curves ‐ much as in the Bourguignon et al. (2005) or the Ferreira et al. (2010) exercises. Behind these counterfactual GICs now lies a full macroeconomic or computable general equilibrium model. To the extent that one trusts the capacity of those models to accurately predict the effects of policies on the general equilibrium of the economy, these GICs are also economically meaningful counterfactuals.<sup>24</sup> That, however, is not a trivial caveat...

# **5. Conclusions**

Income distribution dynamics have long been of interest to economists. As the availability of household survey data for developed and (particularly) developing countries expanded in the 1990s, so did their ability to investigate distributional change empirically. The cross‐country (or “macroeconomic”) literature that sought to exploit international variation in poverty and inequality changes, economic growth and covariates, offered no support for the “grand theories” linking development and distribution. The evidence did not appear particularly supportive of the Kuznets hypothesis, and was inconclusive about the possible effects of initial (or lagged) inequality and poverty on subsequent growth.

That literature did, however, generate three robust stylized facts about growth and distribution in the last couple of decades of the Twentieth Century: (i) there was no statistically significant cross‐country correlation between economic growth and changes in inequality; (ii) so economic growth was strongly

> 24 See the various chapters in Bourguignon, Bussolo and Pereira da Silva (2008) for a set of examples of this “macro‐micro” approach.
# Diasporas<sup>�</sup>

## Michel Beine<sup>a</sup> , Frédéric Docquier<sup>b</sup> and Ça1⁄4glar Özden<sup>c</sup>

> a University of Luxembourg and CES-Ifo

b FNRS and IRES, Université Catholique de Louvain, IZA-Bonn and CReAM-London. c World Bank, Development Research Group Abstract Migration ‡ows are shaped by a complex combination of self-selection and out-selection mechanisms. In this paper, the authors analyze how existing diasporas (the stock of people born in a country and living in another one) a¤ect the size and human-capital structure of current migration ‡ows. The analysis exploits a bilateral data set on international migration by educational attainment from 195 countries to 30 developed countries in 1990 and 2000. Based on simple micro-foundations and controlling for various determinants of migration, the analysis ...nds that diasporas increase migration ‡ows, lower the average educational level and lead to higher concentration of low-skill migrants. Interestingly, diasporas explain the majority of the variability of migration ‡ows and selection. This suggests that, without changing the generosity of family reunion programs, education-based selection rules are likely to have a moderate impact. The results are highly robust to the econometric techniques, accounting for the large proportion of zeros and endogeneity problems. JEL Classi...cation: F22, O15 Keywords: Migration, self-selection, network/diaspora externalities.

> �Earlier versions of this paper have been presented at the "Migration and Development" conference (Lille, June 2008), at the "Globablization and Brain Drain" conference (Tel Avid and Jerusalem, December 2008). The paper bene...tted from comments and suggestions by Luisito Bertinelli, Serge Coulombe, Caroline Freund, Eric Gould, Gordon Hanson, Will Martin, David McKenzie, Mario Piacentini, Samaschwar Rao, Hillel Rapoport, Assaf Razin, Mark Rosenzweig, Maurice Schi¤ and Antonio Spilimbergo. We would like to thank Sara Salomone for gathering data on guest workers’ agreements. The second author acknowledges ...nancial support from the Belgian Federal Government (PAI grant P6/07 Economic Policy and Finance in the Global Equilibrium Analysis and Social Evaluation) and the TOM (Transnationality of Migrants) Marie-Curie research and training network. The ...ndings, conclusions and views expressed are entirely those of the authors and should not be attributed to the World Bank, its executive directors or the countries they represent.

<mark>1</mark>
# **6.2 EMDE financial variables following NIRP announcements: An event study**

The same event study methodology used in section 4.4 is applied in an assessment of the immediate impact of NIRP announcements on financial market developments across EMDEs. Movements in high-frequency indicators on EMDE assets in response to policy changes by BoJ, ECB, and SNB are tracked over time. Isolating the specific effect of NIRP is particularly challenging in an international context, as many more factors come into play, requiring cautious interpretations of the results. Other caveats previously highlighted apply as well.

The approach is comparable to previous studies analyzing the spillover effects of unconventional monetary policies. For instance, Chen et al. (2011) study the spillover effects of QE policies by the U.S. Federal Reserve, BoJ, BoE, and ECB on some EMDEs in Asia and Latin America. They find that earlier asset purchase programs by the U.S. Federal Reserve had larger positive effects on EMDE equity prices and bond spreads, while similar policies by the BoE and ECB led to somewhat mixed results.

On average, the response of EMDE assets is broadly consistent with expectations and previous literature. Currencies appreciated, bond spreads declined, and equity prices increased on the day of the announcement (Figure 19, Table A.3). The average impact on EMDEs is directionally consistent with previous estimates for QE policies by major advanced economies. The reactions of bond spreads, nominal effective exchange rates and equity prices across major emerging markets are of particular interest. Bond spreads declined across the majority of large emerging markets. However, the responses of other emerging markets were rather muted. The positive stockmarket response was broad-based while the exchange rate response varied considerably across countries, both in terms of size and direction. The differences across countries likely reflect domestic developments or other changes in international financial markets on the day of the announcement. If the event window is extended from one day to one month, results show a considerable range of outcomes with no clear pattern, except for a seemingly persistent positive effect on median equity prices (Figure A.4). Mixed results over an extended window likely reflect the influence of a wide range of factors other than just NIRP announcements.

# **6.3 What EMDE policy responses are appropriate?**

While NIRP type unconventional policies in some advanced economies could have immediate positive effects on EMDE financial conditions, these can abruptly change due to the impact of these policies on financial stability, suggesting the need for EMDEs to be ready to implement appropriate policy responses, depending on their cyclical positions and available policy room. Favorable borrowing costs thanks to NIRP might help deploy counter-cyclical fiscal policy, if needed, in EMDEs with adequate fiscal space. NIRP also has implications for EMDE monetary, macro-prudential, and capital flow management policies to help mitigate the implications of changes in external financing conditions.

41