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This can also be translated into initial capital requirements to remain solvent as listed in table 8.

Including potential changes in variability of rainfall, leads to a significant revision of the additional back-up capital requirements for the next 10 years compared to the empirical case: 356% and 481% for the 5% respectively 1% ruin probabilities for MM5 (measured in percent of baseline premium), and about 335% and 455% in both cases for the Precis model. Figure 8 graphically displays those results including confidence intervals.

Table 8: Insurance capital required for financial robustness in 95 (99) percent of the cases over <u>the 10 year time horizon with mean and variability changes</u>

||<br>Back-up capital to<br>avoid insolvency<br>with 95%<br>probability<br>(kwacha/person)|<br>Back-up capital<br>in % of premium<br>(and additional<br>back-up capital<br>necessary<br>compared to the<br>baseline case in %<br>ofpremium)|<br>Back-up capital to<br>avoid insolvency<br>with 99%<br>probability<br>(kwacha/person)|Back-up capital<br>in % of premium<br>(and additional<br>back-up capital<br>necessary compared<br>to the baseline case<br>in % of premium)|
|---|---|---|---|---|
|Empirical|1013.3<br>[1006.4 1020.2]|220|2179.1<br>[2166.3 2191.9]|473|
|MM5<br>near future|2652.5<br>[2641.3 2663.7]|575 (356)|4398.5<br>[4375.7 4421.2]|954 (481)|
|Precis<br>near future|2557.5<br>[2548.0 2567.0]|555 (335)|4276.7<br>[4258.4 4295.1]|928 (455)|
|MM5<br>future|7876<br>[7861 7891]|1708 (1489)|10533<br>[10503 10564]|2285 (1812)|
|PRECIS<br>future|2973.3<br>[2963.1 2983.5]|645 (425)|4687.8<br>[4669.6 4706.0]|1017 (544)|

18
Table 1: Decomposition of Sources of Korea’s Growth of GDP per Capita (%)

|Period|Total|WAP|LFP|AL|HC|K/Y|TFP|
|---|---|---|---|---|---|---|---|
|1960-2014|5.9|0.5|0.5|**1.9**|1.5|1.3|1.1|
|1960-1970|5.0|-0.1|1.2|0.8|**2.2**|1.0|0.5|
|1970-1980|7.4|1.3|-0.3|1.2|1.9|**3.0**|0.7|
|1980-1990|8.6|1.1|1.1|**3.7**|1.7|0.8|2.2|
|1990-2000|6.0|0.3|0.2|**2.3**|1.2|1.9|1.4|
|2000-2010|3.9|0.1|0.2|**2.2**|0.8|0.5|1.3|
|2010-2014|2.5|0.1|0.8|0.5|**0.9**|0.3|0.3|

Note (1) Each column represents the contribution of each variable to the annual average growth rate of GDP per capita.

Note (2) ”Total”: Total growth of GDP per capita, ”WAP”: Contribution of changes of working-age population share, ”LFP”: Contribution of changes of labor force participation rate, ”AL”: Contribution of productivity growth of labor-augmenting technology, ”HC”: Contribution of human capital accumulation, ”K/Y”: Contribution of capital deepening, ”TFP”: Contribution of total factor productivity (which is equal to the labor share times ”AL”) 18
# INTRODUCTION AND SUMMARY

### **THE** QUESTIONS

In this note we attempt to estimate the determinants of the domestic freight transport of countries from cross-section data. Our investigation is guided by an interest in three questions:

- Can the evolution of domestic demand for freight transport be predicted to a reasonably high level of explanation with a few readily available variables? For practical purposes, in decisions on sectoral investment allocations or as background parameters in decisions on individual investments or service projects, that would be a desiraule outcome. Moreover, the smaUl number of usable observations available for our study dictated strict economy in the number of explanatory variables.

- 0Does the explanation differ in a significant way between high-income

- ("developed") and lower-income ("developing") countries? One would expect such differences if the structure of national production (e.g., industry _vs_ agriculture) had an important effect on the demand for freight transport, independent of the effect of measured GDP.

- And does the explanation differ significantly according to economic system: between market economies and others? The formaUy centraly planned economies of Europe are known to be highly transport intensive. In designing schemes for the transformation and privatization of transport enterprises it is important to know whether such high (average) intensity expresses itself also at the margin, and equally for the different major modes?

Our results go some way towards answering these questions.

## METHOD, DATA, AND LMiTATIONS

We investigated the domestic (non-transit) demand for freight transport with a single equation, regressing ton-kilometers on total GDP and country area, on the data of 33 countries.

Since the results are obtained from a cross-section study, they describe long-run behavior or demand for freight transport: the elasticities we obtain are long-run elasticities.

GDP was measured in international dollars as estimated by the International Comparison Project (i.e., converted at purchasing power parities). The chief effect of substituting GDP data converted to US dollars with market exchange rates is to lower significantly the elasticity of ton-
Policy Research Working Paper 7285

# **Abstract**

While scholars agree on the importance of land rental markets for structural transformation in rural areas, evidence on the extent and nature of their operation, including potential obstacles to their improved functioning, remains limited. This study uses household-level data from six countries to start filling this gap and derive substantive as well as methodological lessons. The paper finds that rental markets transfer land to land-poor, labor-rich, and more productive producers throughout. But vast cross-country variation in transfers and the fact that female managers could possibly improve their income by leasing out land point towards barriers to participation that policy might address. Methodological and substantive conclusions are derived.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org; xia.fang.fx@gmail.com; and sara.savastano@uniroma2.it.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
redrew the boundaries for those two places, and the segmentation was done again following the process previously described.

The last challenge with using this segmentation method was that buildings demarcated on OpenStreetMaps do not typically correlate directly with population figures due to factors such as the lack of information on building heights and data being collected through public reporting. Subsequently, a ground-truthing component was designed in order to validate the mauza segmentation. The ground validation exercises yielded several key findings, notably:

- The segmentation of EAs was relatively less accurate in densely populated urban regions as opposed to rural mauzas with scattered settlements.

- The number of households found within these segments varied, with some segments having more households than expected and some having fewer than expected.

Field protocols were designed in order to address these concerns. The protocols involved combining GPS coordinates and also training enumerators in the use of Google Maps to allow for systematic field-based adjustment.

# _Drawing the host sample_

Once the strata were defined, the first stage of selection was done at the mauza level by strata. The selection process used probability proportional to the population size of the mauza. As Table 4 shows, the sample for host communities was divided equally between the two strata, so 33 mauzas were selected in each stratum. Within each selected mauza, three segments were designated with equal probability of selection. Mauzas were allowed to be selected more than once.<sup>34</sup> If this occurred, then the number of selected segments within the mauza increased accordingly. For example, if a mauza was selected twice in the sample, six instead of three segments would be selected from the mauza, each segment containing 100-150 households.

The second-stage selection was made using the enumeration areas created for the selected mauzas. All the segments within a mauza had the same probability of being selected. The field team listed all households within the boundaries of the selected segment, subject to field protocols where necessary.

With the listing data available, the third stage was to select 13 households in each of the listed segments, with every household having the same probability of being selected. The selected households were to be interviewed.

# _Selection probability for hosts_

> 34 Since the selection was done using probability proportional to the population size of the mauza with replacement, bigger mauzas had higher probability of being selected more than once.

16
**Table 4-1: The sector aggregation employed**

|1. Rice paddy and processed|14. Animal products, miscellaneous|
|---|---|
|2. Wheat|15. Wool & silk|
|3. Other grains|16. Vegetable oils & fats|
|4. Vegetables, fruit, nuts|17. Sugar|
|5. Oilseeds|18. Other processed food products|
|6. Sugar beet and cane|19. Beverages & tobacco|
|7. Plant-based fibers|20. Textiles and wearing apparel|
|8. Other crops|21. Extraction|
|9. Livestock|22. Light manufacturing|
|10. Raw milk|23. Heavy manufacturing|
|11. Meat: cattle, sheep, goats, horses|24. Utility and consumption|
|12. Meat: pork & poultry|25. Transport & communication|
|13. Dairy products|26. Other services|

**Table 4-2: The country/region aggregation employed**

|1. Oceania|15. Bulgaria|
|---|---|
|2. East Asia|16. Romania|
|3. South East Asia|17. Croatia|
|4. South Asia|18. Albania|
|5. North America|19. Rest of Eastern Europe|
|6. Latin America|20. Kazakhstan|
|7. Iran|21. Kyrgyzstan|
|8. Middle East and North Africa|22. Armenia|
|9. Sub Sahara Africa|23. Russia|
|10. Switzerland|**24. Ukraine**|
|11. Turkey|25. Azerbaijan|
|12. Rest of EFTA|26. Georgia|
|13. Rest of Europe|27. Rest of Former Soviet Union|
|14. EU_25||

Given the aggregation in Tables 4.1 and 4.2 has been chosen, GTAP-7 calculates the bilateral _ad valorem_ tariffs depicted in Figure 4-1. Figure 4-1 shows for each sector the tariffs levied by the EU on imports from Ukraine, and vice-versa the import tariffs that Ukraine levies on imports from the EU. These _ad valorem_ tariff rates are calculated according to a tradeweighting procedure that combines applied tariffs from the MacMap tariff database into the much more aggregated scheme of the GTAP database. Prior to the simulation of more detailed and more realistic scenarios in subsequent work, these tariffs will checked carefully and updated to account for Ukraine’s WTO accession commitments (see chapter VI below).

28
MALAWI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Benchmarking ICT indicators**

||Unit<br>Lo|w-income countries<br>**Malawi**||Middle-income<br>countries|
|---|---|---|---|---|
|GSM coverage|% population|48.2<br>**93.3**||97.2|
|International bandwidth|Mbps/capita|5.8<br>**0.19**||30.2|
|Internet|subscribers/100 people|0.1<br>**0.15**||2.0|
|Landline|subscribers/100 people|0.8<br>**1.3**||9.4|
|Mobile phone|subscribers/100 people|15.1<br>**5.2**<br>**Malawi**<br>Countries wit<br>access to subm<br>cables|hout<br>arine|86.7<br>Other developing<br>regions|
|Price of monthly mobile basket||**11.0**|11.12|9.9|
|Price of monthly fixed-line basket||**16.5**|13.58|—|
|Price of 20-hour Internet package||**46.6**|67.95|11.0|
|Price of call to the United States||**1.33**|2.59|2.0|
|Price of intra-African calls, mean||**1.10**|0.72|n.a.|

_Source:_ Minges and others 2009. Derived from AICD national database, http://www.infrastructureafrica.org/aicd/tools/data. _Note:_ Mbps = megabit per second; — = data not available; n.a. = not applicable.

## **Table 7. High international call charges driven by both technology and market power**

|$|**Percent of**<br>**cases**|**Call within**<br>**region**|**Call to the**<br>**United**<br>**States**|**Internet**<br>**dial-up**|**Internet**<br>**ADSL**|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0.48|47|111|
| Monopoly on international gateway|16|0.70|0.72|37|120|
| Competitive international gateway|16|0.48|0.23|37|98|

_Source:_ Minges and others 2009.

# Financing Malawi’s infrastructure

To meet its most pressing infrastructure needs and to catch up with developing countries in other parts of the world, Malawi needs to expand its infrastructure assets in key areas (table 8). The targets outlined in table 8 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

19
Table 2 provides a tabulation of the types of firms in terms of industry sector. We again see substantial heterogeneity, with the most common firm types being retail sales, selling and producing food, clothing, education and training, and computer services. The firms are almost entirely domestically focused (only 2 percent export), are mostly owned by men, and the majority have not been undertaking many innovative activities in the three years prior to application: only 16 percent had improved a product line, only 2 percent had any type of quality certification like ISO, 24 percent had trained workers, 6 percent had attended an international exposition or fair, and 13 percent had expanded their market.

Random selection ensures that the treatment and control samples look similar on average: a joint test of orthogonality cannot reject the null hypothesis of balance (p = 0.318).

# **3.2 Take-up**

Firms which were selected in the random draw were invited to workshops in February in which they were told about the procedures for procurement, and the process for filling out a business development plan (BDP). The plan set out what the firm planned to do with the matching grant, and was reviewed by a project management committee for approval. These BDPs were reviewed as received between March and June 2014, and once approved, agreements were signed. Firms were then reimbursed for the agreed services upon completion of the consulting services and providing evidence of payment.<sup>5</sup> Figure 1 shows the main services requested by firms. The most popular requests were for help in marketing, accounting, and worker training.

According to administrative records, 170 firms had signed agreements by October 30, 2014. However, a number of these firms dropped out as a result of concerns about the unstable security situation, and a fuel crisis meaning that they did not want to take the risk of paying 50 percent of the cost of the planned activity. According to the follow-up survey, 57.8 percent of the treatment group used the matching grant in the end.

Table 3 compares the baseline attributes of those firms which did take up the matching grant to those in the treatment group which did not, for the sample answering the follow-up survey. We see that the two groups are not statistically different in most characteristics. This in part reflects

> 5 In some cases, particularly for very small firms or start‐ups, direct payments were made to vendors for the balance of the costs once the firm provided evidence of payment of 50%.

7
As illustrated in Table 6, the curricula of **the** SVS **surveyed** in two of the three provinces (Shaanxi and Liaoning) tend rather more towards specialties transferable among enterprises and/or relevent to the merging 'private' sector -- or self-employment. This is not the case in Hubei Province. Courses in sewing, cooking, accounting, and computer operation fit SVS graduates for a relatively wide range of employers, and represent an entrepreneurial adaptation by the schools to labor market opportunities.

Table 6: Distribution of Curriculum Specializations By Type of Sehool & Sectoral Relevance, Hubei, Shmanzi and Liaoning Provinces, 1986* (In samplo of schools surveyed)

|Province/<br>Type of<br>Sdhool<br>(nLgber<br>InWle)|TotalNuber<br>of<br>Spelalzatlons<br>offered|NWber of<br>Seolallzatlons<br>most relevant<br>toIndustry|Nig,er of<br>Speclalzatlors<br>*mot relevant<br> <br>tocomero<br>and services|
|---|---|---|---|
|**SYS**<br>(5)|19|12|7|
|M1<br>(3)|33|22|11|
|STS<br>(1)|30|14|18|
|SYS<br>(10)|2B|B|20|
|STS<br>(10)|37|21|18|
|SyS<br>(10)|33|10|23|
|SS<br>(10)|25|20|5|
|STS<br>(5)|18|13|3|

Metal led I lsting of coures may be fouid In World Bank (1987) **No SOSB reported from Saanxl.

- 31 -