rafmacalaba/datause-encoder-data huggingface dataset:
# **Asking farmers about transport costs**

13. Households in the IHS2 are asked if they are agriculture producers and what they pay to move their products to the nearest market. These data from the IHS2 are spatially linked to the GIS/HDM4 infrastructure data which provide information on road quality and traffic volumes (a proxy for demand and potential competition). The hypothesis to be tested is that both infrastructure supply and demand have bearing on transport costs. Transportation prices or transport costs paid by farmers are influenced by supply side conditions (road accessibility and quality) as well as the demand side (freight volume to be transported) and the structure of market competition. In addition, network externalities, or economies of scale, are important for transportation industry.

14. A regression analysis is carried out where the dependent variable is a farmer’s transport costs for taking tobacco to markets (in MKs). Results are reported in Table 2. We first control for the volume of tobacco transported (in MKs) and the transport distance (kilometers).<sup>3</sup> After controlling for the (strong) effects of those two variables, the ordinary least squares regressions imply that other policy-relevant factors also significantly affect a farmer’s tobacco transport costs. In particular, **the quality of feeder roads (distance to asphalt roads, kilometers) connecting farms to the main roads is an important factor influencing transport costs** .<sup>4</sup> 15. In addition, road traffic volume data from the GIS+HDM4 data were used in the analysis. For farmers in each location (564 EAs), spatial analysis was used to compute the average road traffic volume (ADT) within a radius of 10 kilometers. The results from the regression show that the transport costs are lower in locations with high traffic volumes. This confirms our hypothesis that **competition and higher trucking demand are associated with lower transport costs** .

**Table 2: The determinants of tobacco transport costs to markets: all tobacco farmers**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent variable:|l|n(a farmer’s tobac|co transport costs|)|
|Estimation||OLS,with IHS2|samplingweights||
|Sample||all tobacc|o farmers||
|ln(transport volume)|0.815***|0.800***|0.812***|0.804***|
||(0.029)|(0.029)|(0.030)|(0.030)|
|ln(transport distance)|0.150***|0.139***|0.148***|0.152***|
||(0.024)|(0.025)|(0.031)|(0.033)|
|ln(distance to asphalt roads)||0.032**||0.014|
|||(0.013)||(0.014)|
|ln(road traffic volume,ADT)|||-0.066***|-0.061***|
||||(0.018)|(0.020)|
|Constant|Yes|yes|Yes|Yes|
|Observations|970|920|825|793|
|R-squared|0.646|0.637|0.665|0.656|

Note: 1. Robust standard errors in parentheses 2. * significant at 10%; ** significant at 5%; *** significant at 1% 16. We also examine the impacts on tobacco transport costs for farmers who use lorries to transport tobacco to markets (see Table 3). For this sub-sample of farmers, the results are similar, but the impacts (coefficient estimates) of feeder road quality and traffic volumes on

> 3 Source:  IHS2 household questionnaire.

> 4 Source:  IHS2 community questionnaire.

- 7 -
significant amount of flexibility, which is a prerequisite of successful restructuring. In other words, labor market in Lithuania seems flexible enough to enable what has been a dramatic reallocation ofjobs and labor.

# **Persistence of High Job Reallocation**

The process of intensive enterprise restructuring, as witnessed by high job turnover rates, has been under way uninterrupted since at least the mid 1990s (Table 6).6 There was some variation in job turnover rates over the period 1996-2000, however it was relatively small, and even when job turnover reached its lowest point, it was still high by international standards. When job turnover was at its peak (1996-1997), the excess job reallocation rate was 28 percent, while when job turnover reached its trough (1999-2000) the rate was still 21 percent, meaning that as much as over ten percent of all jobs were reallocated from shrinking finns to expanding firms. Despite some slow-down, intensive job reallocation continues in Lithuania.

**Table 6 Job Creation and Job Destruction in Lithuania, 1996-2000**

||**1996-97**|**1997-98**|**1998-99**|**1999-2000**|
|---|---|---|---|---|
|**Jobcreationrate**|**14.0**|14.0|13.6|10.5|
|Openings|5.2|4.0|3.8|3.6|
|Expansions|8.8|10.0|9.7|6.9|
|Jobdestructionrate|19.3|12.4|17.7|15.1|
|Closures|7.5|1.9|7.0|1.9|
|Contractions|11.9|10.5|10.7|13.2|
|Net**employmentchange**|-5.3|1.6|-4.1|-4.6|
|Continuingestablishmentsonly|-3.0|-0.4|-0.9|-6.3|
|Jobturnoverrate|33.4|26.3|31.2|25.6|
|Continuingestablishmentsonly|20.7|20.5|20.4|20.1|
|Excessjobreallocationrate|28.1|24.7|27.1|21.0|
|Continuingestablishmentsonly|17.7|20.0|19.4|13.8|

_Source: Annual survey of wages and salaries, various years, Bank staff calculations._

**6** There are no data on job turover **available** for the penod before the 1996.

14
**Table 4. Pervasiveness of NTMs within Economic Sectors.**

**<u>(Coverage Ratios, Percentages)</u>**

||**Type**|||**A: SPS**|||||**B: TBT**|||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Sector .**|**CRI**|**SLV**|**GTM **|**HND**|**NIC**|**CRI**|**SLV**|**GTM **|**HND**|**NIC**|
|01-05|Animal|78.8|100.0|100.0|100.0|90.5|58.8|97.5|95.4|57.5|46.8|
|06-15|Vegetable|68.8|98.4|99.8|85.9|99.8|45.0|32.1|78.8|24.2|91.1|
|16-24|Foodstuffs|83.3|87.5|98.7|78.5|77.7|35.5|85.9|81.8|21.5|34.6|
|25-26|Minerals|93.3|11.8|9.1|2.9|2.8|51.1|52.7|12.5|--|82.2|
|27|Oil Minerals|44.3|16.8|--|2.9|0.0|44.3|97.7|95.2|--|4.6|
|28-38|Chemicals|36.4|9.0|40.8|19.5|13.8|81.4|98.5|74.0|4.5|63.7|
|39-40|Plastic / Rubber|0.2|0.8|--|--|--|1.6|44.1|1.4|--|--|
|41-43|Hides, Skins|44.1|42.1|41.2|--|--|--|28.5|--|--|--|
|44-49|Wood|--|3.8|1.6|--|--|3.8|0.0|--|--|--|
|50-63|Textiles, Clothing|0.3|5.1|4.0|--|--|0.1|--|--|--|--|
|64-67|Footwear|--|0.5|0.0|--|--|--|2.4|--|--|--|
|68-71|Stone / Glass|--|--|--|4.0|2.1|0.3|--|--|--|2.1|
|72-83|Metals|--|--|--|0.0|0.0|0.0|0.3|0.3|--|0.0|
|84-85|Mach/Elec|1.0|--|--|--|--|9.2|0.0|2.9|--|--|

Note: This table reports coverage ratios for SPS and TBT measures across economic sectors in each country in the region. Each cell represents the import value affected by at least one NTM as a share of total sectoral import value. Each country is labeled by its 3-digit ISO code. Source: Authors’ computation Table 5 shows the most important NTMs by their coverage ratio in each country, and shows that registration requirements are particularly common NTMs in Central America – these are highlighted in grey. These requirements are applied not only on importer firms (codes B150 and A150) but also on selected imported products (codes B810 and A810). As agricultural products are important for regional trade, we compare the use of registration requirements for SPS reasons (code A150) – the so called sanitary registries – in Central America to other countries for which data are available (Figure 3). It appears that each of the Central American countries, with the exception of Costa Rica, is a frequent user of this type of regulation.

13
most African trade is transoceanic. Trade among the contiguous eight members of the West African Economic and Monetary Union (WAEMU) represented less than 3 percent of their total trade for each year in the 1990s (Coulibaly and Fontagn ́e, 2006).<sup>4</sup> If anything, one would expect more trade among these countries than other sets of neighbors, because they share a common currency and thus lack one important trade friction. Other cities in the periphery have relationships with their country’s core that are potentially critical to their success. And countries spend to improve those links or simply to reverse decay. Almost $7 billion is invested per year in roads in Sub-Saharan Africa, with a substantial portion funded by donors (World Bank, 2010). Worldwide, transport accounts for 15 to 20 percent of World Bank lending, with almost threequarters of that amount going to roads (World Bank, 2007a).

This paper relates primarily to four bodies of work. The first is on the effect of transport costs on the size and growth of cities and regions. This work has been done primarily using cross-country data (e.g. Lim ̃ao and Venables, 2001) or the construction of very large national transport networks in the United States (Baum-Snow, 2007; Chandra and Thompson, 2000; Atack, Bateman, Haines and Margo, 2010; Duranton and Turner, 2011), India (Donaldson, 2010), and China (Banerjee, Duflo and Qian, 2009). However, no comparable work has been done in Sub-Saharan Africa, which has worse roads, lower urbanization, lower income, and much less industry, and consists of many countries, as opposed to one unitary state.<sup>5</sup> Similarly ambitious transport infrastructure projects have not been carried out in post-independence Sub-Saharan Africa. This paper instead relies on the plausibly exogenous annual changes in transport costs induced by world oil price fluctuations, which allow me to determine the short run impact of shocks better than previous work. This does not mean that the changes were small, however, as average annual oil prices varied by a nominal factor of 7.6 during the period of study (1992–2008).<sup>6</sup> These shocks are also of interest because they are more likely to be repeated in the future.

The second related literature is on the scope and drivers of urbanization and urban economic growth in Africa. This literature is almost exclusively cross-country in nature, so that unobserved country-level factors may be confounding results (Fay and Opal, 2000; Barrios, Bertinelli and Strobl, 2006). An exception is Jedwab (2011), who

> 4The eight countries are Benin, Burkina Faso, Cˆote d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal and Togo.

> 5An exception is Buys, Deichmann and Wheeler (2010), who consider the possible effects of road upgrading on _international_ trade, interpreting the relationship between cross-country trade data and road routes between the largest cities in the context of a gravity model.

> 6This corresponds to a real factor of 5.8 using the United States Consumer Price Index (CPI-U).

3
**Figure 13**

# **Crisis equity betas (2008-2009) vs US Holdings**

This figure presents the scatter plot of betas during the crisis (as country-by-country estimations of local returns vs S&P) against US holdings of foreign equity instruments normalized by equity market capitalization at the end of 2007. ** denotes significance at the 5% level. Source: Didier et al,. 2010, US Treasury, Bloomberg.
# **B. Urban vs. rural India**

In the remainder of our analysis, we contrast urban and rural wage changes for _unskilled_ labor. In particular, we use equation (9) to compute ∆��∆������ ��� separately for uneducated males (figure 15) and for uneducated females (figure 16). On the x‐axis in each panel in the next two figures is the urban‐rural difference in log shares of construction employment (top left), services employment (top right), and female LFP (as a share of all females of working age). The bottom right panel of each of the figures considers the change in the urban (state) population share between the 2001 and 2011 population censuses (see figure B.1 in the appendix).

<!-- Start of picture text --> Tamil Nadu Tamil Nadu<br>Andhra Pradesh Andhra Pradesh<br>Kerala Gujarat Gujarat Kerala<br>Rajasthan Rajasthan<br>Maharashtra Maharashtra<br>Orissa Northern States Orissa Northern States<br>R-sq=.03<br>Bihar Bihar<br>Seven Sisters Seven Sisters<br>R-sq=.34<br>West Bengal West Bengal<br>Karnataka Uttar Pradesh Madhya Pradesh Madhya Pradesh Uttar Pradesh Karnataka<br>-1 -.8 -.6 -.4 -.2 -.4 -.3 -.2 -.1<br>diff. log rel. urb/rur construction share 68-61 diff. log rel. urb/rur services share 68-61<br>Tamil Nadu Tamil Nadu<br>Andhra Pradesh Andhra Pradesh<br>Kerala Gujarat Gujarat Kerala<br>Rajasthan Rajasthan<br>Northern StatesMaharashtraOrissa MaharashtraOrissaNorthern States R-sq=.13<br>Bihar Bihar<br>Seven Sisters Seven Sisters<br>R-sq=.04<br>West Bengal West Bengal<br>Madhya Pradesh<br>Karnataka Uttar PradeshMadhya Pradesh Utt a r Pradesh Karnataka<br>0 .1 .2 .3 .4 0 .2 .4 .6<br>diff. log rel. urb/rur female LFP 68-61 Inter-censal urban pop. share growth<br>DD log-wage uneducated male urb/rural-68/61 Fitted values<br>-.04 -.04<br>-.06 -.06<br>-.08 -.08<br>-.1 -.1<br>-.12 -.12<br>-.04 -.04<br>-.06 -.06<br>-.08 -.08<br>-.1 -.1<br>-.12 -.12<br><!-- End of picture text -->

**Figure 15:  Drivers** **<u>of changes in urban vs. rural wages for males.</u>**

20
51 Decentralization. Policy statements in developing nations often advocate decentralized decision-making and, sometimes, the statutes are even altered to roster decentralization. Some countries have accompanied decentralization policy initiatives with expanded use of grant flows in spite of the obvious conflicts which such financing can create. For example, when the Government of Bangladesh upgraded the thana level of government as the focal point of its decentralization program in 1983, grants to rural governments were increased from Tk 1.82 per capita to Tk 10.12.31 While block grants were used to carry out this transfer of resources, considerable power was maintained by the central government. Furthermore, the large absolute sizes of these block grants provided little incentivt for the local governments to mobilize resources using even the few local revenue instruments provided to them. While the large flow of funds was likely used to strengthen the political backing of the decentralization policy, observers are skeptical that the policy has truly resulted in a transfer of power to the local bodies. 32 Another example of practices which differs from the underlying policy of decentralized autonomy provided to subnational governments has been observed in Nigeria. As stated by Ndongko, "The allocation of revenues by the federal to the state governments since the 1960s has taken place on the basis of two fundamental principles--the principle of derivation and the

> 31See Schroeder, Rural Development Grants to Local Governments in Asia, p.28. 32See Ahmed Shafiqul Huque, "The Illusion of Decentralization: Local Administration in Bangladesh," International Review of Administrative Sciences, Vol. 52, No. 1 (1986), pp. 79-95.
# **Annex 5. Poverty Graph and Severity**

**Figure 22. Albania: Poverty Gap at $2.5/day from Market to Consumable Income**

<!-- Start of picture text --> 2.50% 2.2% 2.3%<br>1.8%<br>2.00%<br>1.50% 1.1%<br>1.00%<br>0.50%<br>0.00%<br>Market Net Market Disposable Consumable<br>Income plus Income Income Income<br>pensions<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

**Figure 23. Albania: Poverty Gap at $4/day from Market to Consumable Income**

<!-- Start of picture text --> 0.09 8.0%<br>0.08 6.8% 7.2%<br>0.07<br>0.06 5.3%<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0.00<br>Market Net Market Disposable Consumable<br>Income plus Income Income Income<br>pensions<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

**Figure 24. Albania: Poverty Severity at $2.5/day from Market to Consumable Income**

<!-- Start of picture text --> 0.01<br>1.1%<br>0.01 1.1%<br>0.01<br>0.01<br>0.6%<br>0.01<br>0.4%<br>0.00<br>0.00<br>0.00<br>Market Net Market Disposable Consumable<br>Income plus Income Income Income<br>pensions<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

**Figure 25. Albania: Poverty Severity at $4/day from Market to Consumable Income**

<!-- Start of picture text --> 3.3%<br>0.04 3.2% 3.2%<br>0.03<br>0.03<br>2.0%<br>0.02<br>0.02<br>0.01<br>0.01<br>0.00<br>Market Net Market Disposable Consumable<br>Income plus Income Income Income<br>pensions<br><!-- End of picture text --> Source: Own estimates using the Albania HBS 2015.

58
Policy Research Working Paper 6687

# **Abstract**

This paper presents a new type of land market analysis relevant to cities with plural tenure systems as in West Africa. The methodology hinges on a systemic analysis of land delivery channels, which helps to show how land is initially made available for circulation, how tenure can be formalized incrementally, and the different means whereby households can access land. The analysis is applied to the area of Bamako in Mali, where information was collected through (i) interviews with key informants, (ii) a literature review on land policies, public allocations, and customary transfers of land, (iii) a press review on land disputes, and (iv) a survey of more than 1,600 land transfers of un-built plots that occurred between 2009 and 2012. The analysis finds that land is mostly accessed through an informal customary channel, whereby peri-urban land is transformed from agricultural to residential use, and through a public channel, which involves the administrative allocation of residential plots to households. The integrated analysis of land markets and land institutions stresses the complexity of procedures and the extra-legality of practices that strongly affect the functioning of formal and informal markets and make access to land costly and insecure, with negative social, economic, and environmental impacts over the long term.

This paper is a product of the Environment and Energy 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 a.durand-lasserve@wanadoo.fr, maylis.dl@orange.fr, and hselod@worldbank.org.

_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
was used the last time the respondent (or the respondent’s child) visited the clinic.<sup>7</sup>

# **3 Experimental design and data**

## **3.1 Overview**

The research project was initiated in 2004 and extended in 2007. Of the 75 rural communities and health facilities, 50 facilities/communities were included in the first phase of the project in 2005 (the _participation & information intervention_ ) and 25 facilities/communities were added in 2007 (the _participation intervention_ ). The catchment area or community for each dispensary was defined as the households residing in the 5-km radius around the facility. A community in our sample does, on average, have 2,500 households residing within the five-kilometer radius of the clinic, of which 350 live within a one-kilometer radius.

For the _participation & information_ experiment, the units (facility/community) were first stratified by location (districts) and then by population size. From each block, half of the units were randomly assigned to the treatment group (25 units) and the remaining health facilities were assigned to the control group. A similar procedure was initiated in 2007 when the project was extended with the _participation intervention_ ; i.e., after stratifying on location and population size, the 25 new facilities were randomly assigned to a treatment group (13 units) and a control group (12 units).

Trial sizes were set to detect effects on utilization and under-five child mortality. The trial sizes were also influenced by logistical and cost constraints and the anticipation of smaller long-run treatment effects. In each community we surveyed approximately 100 households (and collected birth and death statistics from approximately 100 under-five children). Thus, the sample for the _participation & information_ experiment consists of 50 communities/health facilities and approximately 5,000 households. The sample for the _participation_ experiment consists of 25 communities/health facilities and approximately 2,500 households. Mortality and utilization data are available for 2005 and these data were used to estimate study power (Björkman and Svensson, 2009). The estimated overall under-five mortality rate (the number of child deaths per 1000 child-year observations) in the control group in 2005 was 34.1. Assuming 300 child-years of observations in each cluster (three years, 2006-2008, and 100 child observations per year), 50 clusters and an estimated coefficient of variation of 0.15, the _participation & information intervention_ has 80%

> 7While we have no data on the clinicians’ workload, other estimates from similar settings suggest a low workload. For example, combining observational data on time spent per patient with facility records on the number of patients treated per day, Bold et al. (2011) estimate that the total time spent counseling patients per day per clinician in rural primary health clinics in Tanzania and Senegal is only 26 minutes.

4
**–** **<u>Table 2: Trends in Collection of Property Taxes by Economic National Groups, 1990 2014, Percent</u>**

|||<br>**1990–94**|<br>**1995–99**|<br>**2000–04**|**2005–09**|**2010–14**|
|---|---|---|---|---|---|---|
|Property tax as|Advanced market economies|1.8|1.7|1.7|1.7|1.7|
|share of GDP|Emerging market economies|0.4|0.4|0.5|0.5|0.6|
||Low income developing|0.2|0.2|0.2|0.2|0.2|
|Property tax as|Advanced market economies|0.047|0.045|0.046|0.046|0.045|
|share of tax|Emerging market economies|0.013|0.015|0.021|0.02|0.022|
|revenue (%)|Low income developing|0.009|0.007|0.009|0.007|0.005|

_Source:_ IMF Government Financial Statistics. Samples: 36 advanced economies, 62 emerging markets, and 22 low-income developing economies.

16