rafmacalaba/datause-encoder-data huggingface dataset:
http://www.infrastructureafrica.org/library/doc/661/uganda-interactive-infrastructure-atlas

**Table 2. Road conditions and traffic along portions of the Northern Corridor, 2005**

|||Co|ndition|||Type||T|raffic bands (v|ehicles pe|r day)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Corridors|Good|Fair|Poor|Unknown|Paved|Unpaved|Unknown|>300|300–1,000|>1,000|Unknown|
|_Mombasa to Nairobi to_<br>_Kampala to Kigali to_<br>_Bujumbura (Northern_<br>_Corridor)_|**_43.8_**|**_34.7_**|**_9.9_**|**_11.6_**|**_94.6_**|**_0_**|**_5.4_**|**_1.2_**|**_10.3_**|**_80.2_**|**_8.3_**|
|Rwanda|66.5|33.5|0|0|100|0|0|14.5|0|85.5|0|
|Uganda|6.9|34.6|10.1|48|77.5|0|22.5|_0_|21.8|78.2|0|
|Kenya|54.3|34.3|11.4|0|100|0|0|_0_|3.9|83.3|12.7|
|Burundi|45.2|4.2|0|0|100|0|0|0|70.3|25.6|4.2|

_Source:_ AICD analysis.

The cost of moving freight in Uganda varies between typical East African rates of $0.09 on the routes to the coast, and much higher rates of $0.20 on inland routes toward South Sudan. Three reasons underlie the expensive freight tariffs for northbound traffic. First, road quality is extremely poor, which escalates the costs of moving freight. Second, the region is just emerging from conflict, and moving freight in risky environments is typically expensive. Third, trucking companies operating this route are unable to pick-up return freight, returning empty from South Sudan to Uganda, thereby significantly increasing the cost of transport services. Fourth, there seem to be monopolistic market conditions, due to the operation of cartels and interference by local chiefs. This leads to distortion of market prices, which in turn further discourages entry by new operators.

_The speed of Uganda’s road freight is close to the African average, but worse on northbound routes_ . Along most of Uganda’s internal transport routes, surface transport moves at a pace of just under 20 km per hour (kmph), which is relatively good by African standards. But on the northbound routes into South Sudan the effective velocity drops to 8 kmph when delays are fully factored in (table 3). The slow effective velocity of freight in Uganda can be explained by lengthy customs clearance processes and administrative and border crossing delays. Based on estimates from 2008, East African trucking companies have indicated that their trucks have

**Table 3. Benchmarking Uganda’s regional network with African aggregates for regional corridors, 2005**

||**Road in good**<br>**condition (%)**|**Implicit velocity ( km per**<br>**hour)**|**Freight tariff (US cents per**<br>**tonne-km)**|
|---|---|---|---|
|Western|72|6|8|
|Central|49|6.1|13|
|Eastern|82|8.1|7|
|Southern|100|11.6|5|
|**Uganda**|**41**|**8.1–20**|**9–13**|
|_Source:_Ter|avaninthorn and Ra|balland(2009)and World Ban|k staff estimates.|

to wait between 1 and 2 days at Malaba, which is the busiest and most-congested border crossing between Kenya and Uganda. Evidence from 2010 suggests that this delay was reduced significantly, but still 16
# **III. Analytical Framework**

As noted above, the primary data sources that most commonly underpin distributional analysis in India are the NSS data collected by the National Sample Survey Organization. These are cross-sectional surveys that are regularly fielded over time, offering at best a snapshot of consumption-based welfare outcomes at specific moments of time. In this study we are interested in tracking consumption mobility and to pursue this question we convert the NSS surveys into synthetic panels. We implement an approach that has been recently introduced into the literature (Dang and Lanjouw, 2013; Dang, Lanjouw, Luoto and McKenzie, 2014) and provide a brief description below. We also briefly describe the approach followed to specify a vulnerability line, which allows us to then separate our population into our three categories of poor, vulnerable and secure.

- **III.1. Overview of Synthetic Panel and Vulnerability Analysis Methods** Let xij be a vector of household characteristics observed in survey round _j_ (j= 1 or 2) that

are also observed in the other survey round for household i, i= 1,..., N. These household characteristics include variables that may be collected in only one survey round, but whose values can be inferred for the other round. These variables may be roughly categorized in three

# types

- i) time-invariant variables such as ethnicity, religion, place of birth, or parental education,

- ii) deterministic variables such as age (which, given the value in one survey round, can then be determined given the time interval between the two survey rounds),<sup>8</sup> and

> 8 To reduce spurious changes due to changes in household composition over time, we restrict the estimation samples to household heads age, say 25 to 55 in the first cross section and adjust this age range accordingly in the second cross section. This age range is usually used in traditional pseudo-panel analysis but can vary depending on the cultural and economic factors in each specific setting.

12
earmarks can sometimes reduce these tradeoffs; e.g. budget support can be disguised by a donor as aid targeting a particular health issue, as a useful fiction to help sustain domestic support for aid provision.

Another means of making progress toward the Paris goals, without reducing support for the donor’s aid budget, is simply to shift aid allocations so that recipients with stronger management systems are favored even more than they are now. This response could create aid “orphans,” a result not envisioned or advocated in the Paris Declaration. It also has implications for the increasingly-popular practice of ranking donors (e.g. Roodman, 2006; Easterly and Pfutze, 2008). Use of country systems and other Paris Declaration monitoring indicators, aggregated to the donor level, are obvious candidates for inclusion in future rankings (Center for Global Development, 2007). We show in Table 4, however, that recipient characteristics explain more variation in use of country systems than do donor characteristics. Any ranking of donor performance based on the SMPD data should adjust for differences in risk across donors’ aid portfolios. For example, rankings could be based on donor dummy coefficient estimates from two-way fixed effects models that net out recipient effects. If donors were ranked on the unadjusted aggregates, the surest way to move up in the rankings would be to cease giving any aid to countries with less trustworthy systems.

Other possible policy responses to facilitate progress toward the Paris Declaration’s goals go beyond the scope of our model. In particular, we have ignored incentives within donor agencies, but there are ways to raise the reputational stakes for staff members in a country’s development – in effect internalizing more of the benefits of using country systems within the agency. Staff members in many donor agencies arguably are rotated too frequently, shortening the time period in which they need to produce measurable outputs for their annual performance reviews. When staff do need to be re-assigned from one country or region to work on another one, their ongoing projects should remain part of their work programs until they are completed. Even completed projects can be evaluated with respect to their sustained benefits, feeding into current and future performance evaluations.

30
Turkmenistan or Belarus. Some have seen years when PPPs accounted for the bulk of infrastructure investments, most recently Brazil. China has pursued more PPPs than any other country. Yet, on average PPP projects are relatively small and account for less than 1% of China’s exceptionally large investment in infrastructure.

Among regions of the world, Latin America started early and has invested most under PPP contracts – some 38% of the global total. Within the region, however, the interest of different countries shifted. Initially, in the 1990s Argentina was the forerunner in PPPs till the country changed its economic model after the crisis of 2002. Today, Brazil is the big enthusiast. Several East Asian countries such as Thailand and Indonesia were caught up in the early wave of the mid‐1990s. Then China took the lead albeit with smaller projects. In Eastern Europe, early privatization touched mostly telecommunications. Other PPPs took off slowly. The Russian Federation and, most recently, Turkey are most active today. India staid out of the game during the 1990s but then became the global haven for PPPs and project finance around 2010. Sub‐Saharan Africa saw little interest in the 1990s, but has experienced a mini‐boom recently driven by telecommunications and now also electricity PPPs. Only modest activity has been seen in the Middle East and North Africa.

In advanced economies,<sup>17</sup> PPPs have been most popular in Europe led by Britain’s private finance initiative (PFI) that started under John Major. More recently, prior to the financial crisis, they became an approach of choice in Spain and Portugal. In France PPPs always had a home at the municipal level. Some countries are experimenting with PPPs including Germany.<sup>18</sup> Interest may grow in the United States, where PPPs in the transport sector have become a subject of debate.

In sum, there are no simple geographic patterns. Most countries have tried PPPs. Some have seen enthusiasm wax and wane. Others have stuck steadily to some form of PPPs. A new set of countries has so far taken up the banner, when activity flagged elsewhere.

# **Sector patterns**

More pronounced patterns appear when looking at PPPs by sub‐sector of infrastructure. Telecommunication investments typically led, notably in Eastern Europe and Central Asia, the Middle East and in Sub‐Saharan Africa. Electricity was next in line. Within the power sector, the emphasis has been on independent power production (IPPs) with limited activity in transmission and distribution. Very little overall has happened in water and sanitation. There, the major game was Chinese PPPs for water treatment plants. Roads dominate transport sector investments.

> 17 For advanced economies data on PPPs are not systematically available over a longer period as for developing economies

> 18 Wagenvoort R. et al. (2010) “Infrastructure Finance in Europe: Composition, Evolution and Crisis Impact” in _EIB papers Vol. 15 No. 1_

6
(1998) for reviews on the damaging consequences of corruption in an economy. Most recently, Ayyagari, Demirguc-Kunt, and Maksimovic (2010) use data on bribe payments across 25,000 firms in 57 countries and show that corruption is indeed a tax on innovation as suggested by Murphy, Shleifer, and Vishny (1994). They find that innovating firms pay more bribes to government officials than non-innovating firms and that innovating firms that pay bribes do not receive better services than firms that do not bribe.

An alternate second mode of government intervention is through connections with the firms. There has been a large literature analyzing the benefits and disadvantages of political connections. One strand of this literature focuses on identifying connections between individual firms and government officials and shows how these connections increase firm value (e.g. Fisman, 2001; Johnson and Mitton, 2003; Faccio, 2004; Goldman, Rocholl and So, 2009). Faccio, Masulis, and McConnell (2006) find that politically connected firms are significantly more likely to be bailed out than similar non-connected firms suggesting that these firms receive financial assistance during periods of financial distress. Fan, Wong, and Zhang (2007) find that Chinese firms with politically connected CEOs underperform those without politically connected CEOs in terms of stock returns, earnings growth, sales growth and return on sales.

Finally, a third strand of the literature focuses on how political favors influence financing. Dinc (2004) provides cross-country bank-level evidence in 22 emerging markets of politically motivated lending at government owned banks. He shows that government owned banks increase their lending in election years relative to private banks. Other studies have taken a detailed single-country approach to analyze the consequences of government lending. Khwaja and Mian (2005) use loan-level data from Pakistan and find that government banks differentially favor politically connected firms by providing greater access to credit - firms that are politically connected borrow 45% more but also have 50% higher default rates. Sapienza (2004) also finds that Italian public banks charge lower interest rates than private banks. Further,, the stronger the political party in the area where the firm is borrowing, the lower the interest rates charged. Similarly, Cole (2009a) finds that in India, the amount of agricultural credit lent by government banks is higher in election years. All these studies point to the costs associated with politically motivated loans such as higher default rates and lower recovery rates. These studies are also consistent with the broader literature on how government ownership of banks in developing 35
(2009) for the Mombasa-Nairobi corridor in East Africa (1.88 times that in the U.S.) and for the Bamako-Accra corridor in West Africa (3.28 times that in the U.S.).

# **6 Bridging the Methodologies**

I finish the review with a comparison of methodologies laid out so far and a discussion of how they can be used in tandem. Table 1 provides a summary of data requirements, types of transport costs inferred and challenges for each.

As described above, estimation based methods recover trade costs at large, including frictions other than transportation costs. In their review of international trade costs, Head and Mayer (2013) make an accounting exercise using observable international shipping costs,<sup>9</sup> and a range of reasonable parameter values for the elasticity of trade to trade costs. Their results suggest that "dark trade costs," i.e., what cannot be accounted for and is therefore a residual, explains between 50-85% of the effect. Similar to the literature trying to explain per capita income differences across countries using observable capital and labor stocks, a large unexplained residual remains as the measure of our ignorance. In the context of domestic trade, this residual component is expected to be smaller since some of the border-related frictions are absent. Still, an expected gap is an important point to keep in mind for practitioners when they compare estimated trade costs with survey-based or imputed transport costs.

Such comparisons, however, are valuable in that they provide benchmarks across studies. Similar to the practice by Atkin and Donaldson (2015) described above, Porteous (2019) leverages information generated by different methodologies. After estimating trade cost for agricultural commodities in Africa, he compares the values for the corresponding corridors with the trucking costs from Teravaninthorn and Raballand (2009). His trade cost estimates are 50 to 100% higher than survey-based freight costs, which, per the discussion above, is an expected outcome since price-differentials capture more than transport costs. The fraction accounted for freight is informative about what policy makers can hope to achieve in terms of boosting trade and economic activity with policies and investments that can affect transport costs in the short run.

Despite its shortcomings listed above, it is the structural gravity model that enables predicting the GDP impact of particular transportation projects. A data challenge in linking gravity-based and survey-based measures in a developing country context is the fact that data on within-country trade flows is generally lacking in such environments. It is precisely for this reason that notable attempts to estimate trade costs in Africa cited above (e.g., Atkin and Donaldson, 2015; Porteous, 2019) use price data. Prices,

> 9To estimate transport and insurance costs, they use the difference between reported cost-insurancefreight (CIF) and free-on-board (FOB) prices in customs records.

18
treatment. To create a counterfactual for the subgroup of households with children attending the main school in the treated districts, we also chose a subsample clustered around the main schools in the districts of the control group to serve as a control group for the school intervention.

In what follows we assess the impact of the two treatments: mass media and community on a wide range of outcomes and we also study separately the school level component in the community intervention. We will refer to the households employed to explore the community treatment as _community sample_ and to the households used to investigate the . school component of the community treatment as the _school sample_

# **4   Data Collection**

We conducted baseline and follow-up surveys. The baseline survey was conducted from May through August 2008 in a total of 3,576 households. The follow-up took place four months after the project activities ended—from March through June 2011. We located 2,847 of the original households interviewed for the baseline survey during the follow-up round. We replaced the households we could not find with new households that fit the following criteria: the family had lived in the dwelling for at least the two previous years (e.g., when the intervention took place) and had a child under two years of age at the time of the baseline survey.

For the household-level sampling, in each of the 125 districts allocated to treatment and control groups, we randomly selected between 15 and 20 households from a census conducted prior to our baseline survey that listed all households with children under two years of age. From the 44 districts assigned to the community treatment, we randomly selected an additional set of 15 to 20 households with children under two years old and with at least one sibling attending the main treated schools in each district to assess the effect of the school subcomponent of the district-level intervention. To serve as counterfactual for this last group, in each of the 41 districts allocated to the control group, we randomly chose a second set of 15 to 20 households with children under two years old 13
_(Box 2)_ Khurutshe area of Kgatleng District. Results indicated that climate change could lead to an annual increase of more than 20% in cattle water demand by 2050 because of increased temperatures. At the same time, a decline is likely in the contribution of surface pan water to cattle water supply, leading to substantial increases in the abstraction of groundwater for cattle. While not much is known with any certainty concerning the recharge rates of underground aquifers in Botswana, the problems of water supply for increasing livestock populations are likely to be exacerbated by climate change in the coming decades.

Policy mechanisms may be needed to address this problem. From the livestock side, one way may be to control livestock numbers explicitly, managing stocking rates according to the recharge rates of the aquifers that supply livestock with water. Another option might be through the use of tradable pumping permits, which would allow water allocation issues and water abstraction rates to be controlled. During drought years, the Khurutshe aquifer supplies the city of Gaborone and outlying areas with water, and increased reliance on groundwater for both cattle production and urban water supply may lead to competition and sustainability issues. A third option may be to increase the efficiency of utilizing surface water supplies for livestock production (e.g. through fencing, lining pans, and excavating new pans in strategic locations), via community management.

Sources: Masike (2007); Masike and Urich (2008).

27
# 1 Introduction

According to official rhetoric, countries involved in a regional trade agreement (RTA) expect a welfare gain. This expectation is so strong that most engage in many different agreements leading to what Bhagwati called the “spaghetti bowl” phenomenon, that is the crisscrossing of many regional agreements differing in their schedules of phasing out tariffs, rules of origin and excluded products. Recent studies of trade effects of developing RTAs come to different conclusions, sometimes for the same RTAs, as depicted in Table 1.

Table 1: Trade impact of some developing RTAs

||Net trade creation|Net trade diversion|
|---|---|---|
|AFTA/ASEAN|Carrère (2004)<br>Elliott & Ikemoto (2004)<br>Gosh & Yamarik (2004)<br>Cernat (2001)|Dee & Gali (2003)<br>Soloaga & Winters (2000)|
|LAFTA/LAIA|Dee & Gali (2003)<br>Gosh & Yamarik (2004) <br>Soloaga & Winters (2000)|Carrère (2004)<br>Soloaga & Winters (2000)|
|MERCOSUR|Gosh & Yamarik (2004)<br>Cernat (2001)<br>Soloaga & Winters (2000)|Carrère (2004)<br>Dee & Gali (2003)<br>Krueger (1999)|

For instance, AFTA, LAIA and MERCOSUR appear to have been net trade creating in some studies and net trade diverting in others. These studies use different estimation methods, different databases and different dynamic specifications to measure trade effects, and they focus on the number of years these RTAs have existed to estimate their trade impact.

Freund and McLaren (1999) introduced an alternative way of looking at RTAs trade effect by focusing on the dynamic of trade orientation when a country joins a regional trade agreement and over the number of years of membership. This paper follows this idea of evaluating the participation effect of each RTA's member. To carry out such analysis, we propose an RTA variable taking into account the number of years of participation of each member, and we use a two-step estimation approach combining a gravity model estimation and a kernel regression of the estimated trade residuals. We focus on seven developing RTAs covering Sub-Saharan Africa (ECOWAS and SADC), Asia (AFTA and SAPTA) and Latin America (CACM, CAN and MERCOSUR) over the period 1960-1999.<sup>1</sup> The results indicate that except for SAPTA, all these RTAs have had a positive impact on their members' intra-trade over the estimation period (1960-1999). AFTA

> 1Appendix 1 describes these RTAs.