rafmacalaba/datause-encoder-data huggingface dataset:
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Air transport

## **Achievements**

The Benin air transport market is characterized by significant airline competition. The presence of three local airline companies and several large foreign operators in this medium-sized market seems sufficient to ensure a certain level of competition, even though the carriers share routes rather than compete directly with each other (World Bank 2007b). The relatively high level of competition may be attributable to the regional implementation of the Yamoussoukro decision, which liberalizes international regional air traffic.

Competition in the market has enabled Beninese air transport traffic and connectivity to recover since the global decline in 2004. Total traffic (excluding domestic) surpassed the baseline of 572,433 in 2001, reaching 603,436 in 2009 despite the global recession. Figures show a strong increase from 2007 to 2009, with solid growth in traffic between other countries in Sub-Saharan Africa, in traffic between North Africa and Benin, and in intercontinental traffic (figure 10a). Five of the airlines serving Benin established their routes between 2004 and 2009, including Kenya Airways, increasing the number of international city pairs from 16 to 18. New airlines took over the capacity left by Air Afrique after its collapse in 2001. The total number of city pairs as of 2009 was 19, recovering to precrisis levels (figure 10b).

**Figure 10. Evolution of seats and city pairs in Benin**

<!-- Start of picture text --> a. Seats b. City pairs<br>700,000 25<br>600,000<br>20<br>500,000<br>15<br>400,000<br>300,000<br>10<br>200,000<br>5<br>100,000<br>- 0<br>2,001 2,004 2,007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total International Intercontinental Domestic<br>Total International Intercontinental Domestic<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source_ : Bofinger 2008. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note_ : As reported to international reservation systems. No domestic services are represented in the statistics. The capacities shown in panel 10.a reflect the fleet of Air France, Royal Air Maroc, and a dozen other carriers.

Carriers in Benin are using modernized air fleets. The main foreign companies operating in Benin have modernized 88.5 percent their fleets and, as of today, the country is served by a comparably new fleet in the region (table 7).

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# **Table 4. Verification of Randomization**

|||All me|mbers|||M|ale|||Fe|male||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Mea|ns|P-val of||Mea|ns|P-val of||Me|ans|P-val of|
||N.<br>Obs.|Male<br>Brochure|Female<br>Brochure|t-test<br>(2)-(3)|N.<br>Obs.|Male<br>Brochure|Female<br>Brochure|t-test<br>(6)-(7)|N.<br>Obs.|Male<br>Brochure|Female<br>Brochure|t-test<br>(11)-(12)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Characteristics at Baseline**|||||||||||||
|Age|3,451|37.95|37.81|0.74|1,880|37.98|38.40|0.89|1,571|37.91|37.20|0.85|
|Number of children under 9|3,451|1.73|1.83|0.77|1,880|1.92|1.99|0.53|1,571|1.45|1.40|0.17|
|Business Owner(Yes=1)|3,451|0.60|0.59|0.57|1,880|0.63|0.59|0.75|1,571|0.57|0.60|0.79|
|Digit Span Recall|3,451|3.36|3.28|0.85|1,880|3.82|3.88|0.83|1,571|2.72|2.65|0.82|
|Risk Tolerance (0=Risk Averse; 10= Risk Lover)|3,451|3.71|3.52|0.25|1,880|3.81|3.81|0.53|1,571|3.57|3.22|0.49|
|Land|3,451|4.38|4.58|0.82|1,880|5.83|6.05|0.77|1,571|2.36|3.05|0.55|
|Years of Education of Spouse|3,451|3.69|3.71|0.69|1,880|2.72|2.73|0.86|1,571|5.05|4.75|0.55|
|Member of a Mixed Group (Yes=1)|3,451|0.04|0.08|0.10|1,880|0.02|0.06|0.06|1,571|0.07|0.10|0.89|
|Own Education|3,451|4.26|3.95|0.14|1,880|5.27|5.36|0.78|1,571|2.84|2.47|0.07|
|Decision Making|3,451|2.74|2.57|0.35|1,880|3.41|3.26|0.77|1,571|1.82|1.85|0.61|
|**Pct. Borrowing from ... at the time of Baseline***|||||||||||||
|Commercial Bank|2,931|2.38|1.7|0.64|1,616|3.69|2.71|0.60|1,315|0.51|0.69|0.74|
|Microfinance Institution|2,931|71.34|67.62|0.30|1,616|69.44|63.74|0.98|1,315|74.06|71.74|0.46|
|Friends and Relatives|2,931|7.71|6.58|0.48|1,616|7.85|5.16|0.05|1,315|7.5|8.09|0.14|
|Informal lenders|2,931|0.7|0.2|0.07|1,616|0.83|0.26|0.19|1,315|0.51|0.13|0.23|
|**Pct. Offered Business Training**|3,451|52.47|51.82|0.83|1,880|51.29|52.03|0.93|1,571|54.1|51.6|0.48|
|<br>**Member is eligible for loan lottery (Yes=1)**|3,451|63.72|69.00|0.54|1,880|64.63|59.69|0.82|1,571|62.45|62.21|0.66|
|P-val of F-test that all baseline characteristics are<br>jointly insignificant||||0.65||||0.58||||0.65|

Notes: * denotes variable measured at follow-up, conducted in December 2008. Pct. Offered Business Training and Member eligibility come from administrative data from NRSP.
Buddenberg and Byrd (2006) note that in Afghanistan, national accounts are presumed to underestimate the economic value of the illegal drug industry, resulting in a potential downward bias of about one third in national income estimates. In contrast, they note that self-reports of poppy production by farmers in the national household survey for Afghanistan are substantial and do not appear to suffer from significant nonresponse problems. Despite these measurement concerns, it is useful to note that unlike household consumption and income surveys, the global community has invested significant efforts in research, training and in general human capital development to produce reasonably harmonized measures of national income.

A final point to make on the observed gaps between household surveys and national accounts is to recognize that the objectives of both instruments differ. Likely of greatest relevance, household surveys are meant to measure the distribution of well-being of people (along many dimensions and frequently with a greater emphasis on the less well off), while national accounts are focused on measuring aggregate income and productivity (not the distribution of well-being). There is a long tradition of critiquing national accounts as a measure of well-being.

Stiglitz, Sen and Fitoussi (2009) summarize many of these points noting that national income does not account for within-country distribution of income, is not monotonically increasing in well-being (e.g. traffic jams consume fuel, increasing national income but do not improve well-being), nor does it capture certain types of activities that contribute to well-being (e.g. unpaid household labor).

The overall objective of the System of National Accounts (SNA) is to produce an aggregate statistic. Deaton (2005) notes that SNA data tend to include larger transactions with greater probability than smaller transactions, and that to some extent this is intentional. The SNA training instructions specify that greater effort should be directed at larger transactions. Deaton cites OECD (2002, p. 179) where the SNA training instructions with respect to valuing home-production state that the time expended to collect this information should only be expended if the amount produced is sufficiently large with respect to the total supply in the nation of that particular item. In contrast, household budget surveys, and living standards surveys tend to focus on home production and more generally, include smaller transactions with greater probability than larger ones. The primary objectives of these surveys are typically to rank households, identify the poor, and measure consumption patterns. Conversely, estimates of 12
measures. As stated above, the original motivation to introduce Landsat imagery was due to the expected difficulty of nightlights to capture very small but highly productive units. This section lends evidence to this by performing additional analysis of the prediction power of Landsat and nightlights by dividing the data into subsamples.

At first, the sample is divided by the share of formal employment. Since the data only measures economic activity of formal enterprises, there is concern that the share of informal employment will be correlated with the satellite data and thus will introduce a non-classical measurement error. To check this, the paper follows McCaig and Pavcnik (2015) and calculates the share of informal workers at the district level using the 2009 Population and Housing Census.<sup>11</sup> The sample is then split into deciles of formal employment and run separate regressions for log employment at the commune level for each sub-sample.<sup>12</sup> Figure 3 shows the resulting R-squared measures for three specifications: 1) Sum of Lights, 2) a specification with only the two Landsat spectral indices (NDBI and NDVI), 3) the first two moments of all Landsat spectral bands. In the upper panel of Figure 3 one can see that, as expected, the Landsat measures perform extraordinarily well in communes with a high share of formal employment. These communes tend to be cities with smaller area size. The flexible Landsat specification dominates the nightlight approach at all deciles, while the simple indices are superior to the SOL measure only for communes with a high share of formal employment.

Next, the sample is split up into deciles of commune size and again regress individual models for each decile. The lower panel of Figure 3 shows the resulting R-squared measures according to commune size. As expected, the sum of lights approach does extremely poorly at predicting economic activity of very small communes. The prediction power then increases sharply for the next decile and then decays almost monotonously with size. In contrast, both

> 11McCaig and Pavcnik (2015) define the share of informal employment by the share of all workers either being self-employed or working in family and farm businesses.

> 12Due to the uneven number of communes within a district, the decile bins do not contain exactly the same number of communes.

18
from Puerto Rico and Guam with their wages back home. Since there are no governmental restrictions on migration, the implied wage differential (in equilibrium) reflects migration costs. Clemens et al. (2019) obtain predicted wage differentials for Puerto Rico and Guam in the 1.3-1.5 region, indicating a psychic cost hovering between 20% and 30%.<sup>11</sup> For our main specification, we take a lower bound for the psychic cost _τid_<sup>_ψ_of 20%.Finally, we consider</sup> a tax schedule on earnings in the US, _{τid_<sup>_w}_,whichgoesfrom10%to37%.12</sup>

# **4 Results**

The results of our calibration are depicted in Figure 2, where we start with assuming no taxes on the work permits.

With the choice of parameter values described above, we find that the two curves intersect at a work permit price of around 20,000 dollars with 33 million permits traded (Table 1, column 1). To look at the margins of impact, we first look at the benefits to the permit sellers. First, permit sellers see their income increase by the price of the permit net of their current earnings. On the aggregate, US$656 billion are paid from buyers to renters, who then forgo US$394 billion in before-tax earnings. As a result, a market for work permits increases the monetary income of permit sellers by US$262 billion. To this tally, one should add the welfare gains from leisure, which, set at 10% of income, amounts to another US$39 billion. Thus, for permit sellers, the scheme roughly doubles their welfare.

On the immigrant side, their higher productivity allows a net increase in output of 6.7%

> 11Notice that we express migration costs as a share of the US earnings while Clemens et al. (2019) write them as a share of earnings at the origin. Following Clemens et al. (2019), let the earnings differential (US relative to Mexico) needed to compensate for the migration cost be 1 + _π_ for _π >_ 0. Then the migration cost as a share of US earnings is _π/_ (1 + _π_ ). The values for _π_ ˆ of 0.3-0.5 found by Clemens et al. (2019) based on wage differentials between Puerto Rico/Guam and the US mainland imply that migration costs as a share of US earnings of 0.23-0.33. On also allowing for out-of-pocket travel costs and rounding to the same order of accuracy as Clemens et al. (2019), the implied range for the total of the extra cost of living and the migration in our analysis is 0.2-0.3, as a share of US earnings.

> 12More specifically, we follow the IRS’ 2018 Tax Rate Schedule ( `https://www.irs.gov/publications/ p17` ) and use the following rate profile: 10% for yearly income below US$9,700; 12% for yearly income below US$36,475; 22% for yearly income below US$84,200; 24% for yearly income below US$160,725; 32% for yearly income below US$204,100; 35% for yearly income below US$510,300; 37% for yearly income above US$510,301.

13
Although in some symbolic respects the disadvantaged position of the Jatabs did become less obvious over time (for example, Jatabs gradually became able to sit on string cots alongside other castes, and were eventually allowed to draw water from the same wells) their weak position remained clear. Jatabs continued to endure many forms of discrimination, not only on the part of fellow villagers but also from government officials. They had, for example, been a prime target of extortion by urban-based managers of the local credit cooperative (see chapter 9, by Drèze, Lanjouw and Sharma, in Lanjouw and Stern, 1998).

“Social development” in the dimension of caste relations in Palanpur was thus a mixed process. On the one hand there was clear evidence of the erosion of the dominant position of the Thakurs in the village hierarchy. The Thakurs were increasingly being challenged by the Muraos – a caste whose ability to take advantage of the opportunities offered by agricultural change had been remarkable. Yet, from the point of view of the poorest caste in Palanpur, their relative position improved hardly at all. Few of the major changes and events which had taken place in Palanpur over the survey period appeared to exercise any positive impact on the relative position of Jatabs in the village society.

We shall see below that standard inequality decomposition analysis does not capture well this portrayal of different caste fortunes during the study period. The ELMO (2008) adaptation, on the other hand, produces a statistic which captures at least some elements of the story briefly presented above.

# **4.  Inequality in Palanpur and Group Differences**

We consider in this section the contrasting performance of the conventional betweengroup inequality contribution and the partitioning index derived from the ELMO (2008) 16
|P063643|2000|2000|5/23/2000|2 Closed|Colombia|
|---|---|---|---|---|---|
|P063643|2000|2001|6/28/2001|4 Closed|Colombia|
|P063644|2002|2002|5/24/2002|1 Closed|Ecuador|
|P063644|2002|2003|6/12/2003|4 Closed|Ecuador|
|P063644|2002|2004|6/24/2004|7 Closed|Ecuador|
|P063644|2002|2005|4/11/2005|9 Closed|Ecuador|
|P063644|2002|2005|6/29/2005|10 Closed|Ecuador|
|P063644|2002|2006|9/5/2005|11 Closed|Ecuador|
|P063644|2002|2006|2/21/2006|12 Closed|Ecuador|
|P063644|2002|2006|6/29/2006|13 Closed|Ecuador|
|P063644|2002|2007|12/1/2006|14 Closed|Ecuador|
|P063644|2002|2007|6/22/2007|15 Closed|Ecuador|
|P063644|2002|2008|12/4/2007|16 Closed|Ecuador|
|P063644|2002|2008|6/25/2008|17 Closed|Ecuador|
|P063644|2002|2020|7/14/2019|18 Closed|Ecuador|
|P063656|2002|2002|6/28/2002|3 Closed|Lithuania|
|P063656|2002|2003|12/23/2002|4 Closed|Lithuania|
|P063683|2001|2001|6/20/2001|1 Closed|Africa|
|P063683|2001|2002|5/13/2002|3 Closed|Africa|
|P063683|2001|2003|5/27/2003|8 Closed|Africa|
|P063683|2001|2004|6/30/2004|12 Closed|Africa|
|P063683|2001|2005|6/13/2005|14 Closed|Africa|
|P063683|2001|2006|12/21/2005|15 Closed|Africa|
|P063683|2001|2006|6/22/2006|16 Closed|Africa|
|P063683|2001|2007|2/8/2007|17 Closed|Africa|
|P063683|2001|2007|6/28/2007|18 Closed|Africa|
|P063683|2001|2008|12/20/2007|19 Closed|Africa|
|P063683|2001|2008|6/25/2008|20 Closed|Africa|
|P063683|2001|2009|12/18/2008|21 Closed|Africa|
|P063683|2001|2009|5/14/2009|22 Closed|Africa|
|P063683|2001|2010|12/27/2009|23 Closed|Africa|
|P063683|2001|2010|5/2/2010|24 Closed|Africa|
|P063683|2001|2011|1/29/2011|25 Closed|Africa|
|P063683|2001|2020|5/13/2020|26 Closed|Africa|
|P063717|1999|2000|6/30/2000|4 Closed|Red Sea and Gulf of Ad|
|P063717|1999|2001|6/26/2001|6 Closed|Red Sea and Gulf of Ad|
|P063717|1999|2002|6/20/2002|9 Closed|Red Sea and Gulf of Ad|
|P063717|1999|2003|6/2/2003|13 Closed|Red Sea and Gulf of Ad|
|P063717|1999|2004|2/19/2004|15 Closed|Red Sea and Gulf of Ad|
|P063717|1999|2005|4/21/2005|18 Closed|Red Sea and Gulf of Ad|
|P063717|1999|2006|11/16/2005|19 Closed|Red Sea and Gulf of Ad|
|P063732|1999|1999||1 Closed|Indonesia|
|P063732|1999|2000|6/27/2000|3 Closed|Indonesia|
|P063732|1999|2001|6/20/2001|5 Closed|Indonesia|
|P063732|1999|2002|6/26/2002|7 Closed|Indonesia|
|P063732|1999|2003|6/13/2003|9 Closed|Indonesia|
|P063732|1999|2004|11/13/2003|10 Closed|Indonesia|
**<u>Table 5: Frequency, production value, and number of diary entries for different crops</u>**

||**Freque**|**ncy (%)**|**Productio**|**n value ($)**|**No. of entries**|
|---|---|---|---|---|---|
||**Diary**|**Recall**|**Diary**|**Recall**|**Diary**|
|**_Cash crops_**||||||
|Coffee|21.69|31.89|10.63|29.69|6.99|
|Rice|12.29|5.43|15.74|8.96|5.11|
|Cotton|0.57|9.14|0.50|6.12|2.61|
|Sugarcane|17.36|4.21|0.37|3.85|6.64|
|All cash crops|||27.24|48.62||
|**_Food crops seasonal_**||||||
|Maize|73.93|76.80|66.45|60.38|11.41|
|Groundnuts|41.42|30.37|46.94|15.14|6.94|
|Beans|78.36|71.67|124.38|37.71|21.49|
|Finger millet|30.32|25.72|31.66|11.48|7.38|
|Sorghum|27.44|22.50|14.48|5.80|4.65|
|<br>Field peas|17.21|1.87|9.59|0.34|6.34|
|Simsim|3.16|7.87|2.36|3.23|3.35|
|Soybeans|3.38|4.26|1.57|1.32|2.89|
|All seasonal food|||297.43|135.40||
|.. excl beans|||173.05|97.69||
|**_Food crop continuous_**||||||
|<br>Banana|75.62|59.40|120.03|98.14|34.11|
|Sweet potatoes|83.16|59.02|81.09|33.25|28.66|
|<br>Cassava|82.01|58.82|55.36|41.93|26.47|
|Irish potatoes|28.03|7.92|9.53|3.54|8.61|
|<br>Yam|36.72|7.05|9.57|1.21|8.17|
|All cont. food|||275.58|178.07||
|**_Fruit & Vegetables_**||||||
|Tomatoes|22.19|3.39|3.76|3.63|5.57|
|Onion|7.99|1.74|1.63|1.71|4.02|
|Passion fruits|13.06|0.87|1.80|0.73|4.95|
|Avocado|25.80|2.84|1.93|0.45|5.72|
|Pineapples|12.94|2.81|0.75|1.47|4.04|
|Cabbage|4.03|1.29|0.60|1.35|4.33|
|<br>Pawpaw|32.71|1.72|1.46|0.22|7.44|
|<br>Oranges|15.85|0.72|1.37|0.13|5.67|
|<br>Mango|18.08|1.82|1.04|0.40|4.56|
|<br>Dodo|6.57|0.65|0.24|0.50|7.20|
|Egg plants|4.03|1.00|0.29|0.26|4.44|
|All fruit & vegetable|||14.87|10.85||
|<br>**_All crops_**|||**615.12**|**372.94**||

_Source:_ Own computation from harvest diary and 2005/06 UNHS Only complete diaries included.

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trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations.

The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) –important in a volatile crisis situation– and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators.

The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees. Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper.

# **2. The Listening to Displaced People Survey**

The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household.

Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et 3
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