rafmacalaba/datause-encoder-data huggingface dataset:
to reform and raise capacity in trade facilitation?” “What are the implications for South Asia from global reform?” We set the target level of each trade facilitation indicator at halfway up to the East Asia average. The same scenario also is employed for the rest of the world in order to make a comparison possible between South Asia’s trade gains from its intra-regional trade and its trade with the rest of the world. The analysis will focus on trade between South Asia and the rest of the world within the sample of 76 countries. (We do not include gains from capacity building between countries in South Asia or gains between countries within the group of the rest of the world.) As shown in Table 3, the total gains to South Asia from unilateral capacity building in the region are estimated at approximately $27 billion. This represents about 32 percent of the region’s trade with the rest of the world. It is important to note that 87 percent of the total gains to South Asia are generated from _South Asia’s own moves_ to upgrade infrastructure in ports and information technology, harmonize regulations, and improve customs. This stresses the importance of capacity building in trade facilitation in South Asia as a means of strengthening global ties outside the region -- as well as fostering intra-regional trade. The most promising area for focus appears to be service-sector infrastructure as demonstrated in Figure11. The gains from capacity building in service-sector infrastructure should also be considered along with the significant gains from associated with raising port efficiency. This is similar to the conclusions reached in the analysis of intra-regional action above.

Table 3 indicates that the relative importance of South Asia’s capacity building to that of the rest of the world. By construction, the gains to the South Asia region from unilateral capacity building coincide with the gains to the rest of the world from its partners’ (South Asia’s) capacity building. Trading partners outside of the region clearly gain from improvements made in South Asia. As indicated in Figure 9, the average performance in trade facilitation in South Asia is lower than that in East Asia’s and the sample average. Therefore, raising capacity in South Asian countries could significantly contribute to both trade expansion within the region and outside of the region.

Table 3: Trade Gains ($ million) from Unilateral and Collective Capacity Buildings between South Asia and the Rest of the World

||Port eff<br>(Maritime|iciency<br>and Air)|Cust|oms|Regu|lation|Service<br>infrastr|-sector<br>ucture|To|tal|
|---|---|---|---|---|---|---|---|---|---|---|
||unilateral|partners|unilateral|partners|unilateral|partners|unilateral|partners|unilateral|partners|
|South Asia|8,421|1,268|3,881|755|3,809|836|15,452|1,941|27,560|4,800|
|Rest of the world|<br>1,268|8,421|755|3,881|836|3,809|1,941|15,452|4,800|27,560|

Source: Authors’ calculation based on Wilson, Mann, Otsuki database 2004 Note: “The rest of the world” includes a group of 76 countries excluding the South Asian countries.

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Table 3 Continued: Descriptive Statistics: Commerce sector

||Mean|sd|p10|p25|p50|p75|p90|N|
|---|---|---|---|---|---|---|---|---|
|Change 2008-2013|||||||||
|**ICT variables**|||||||||
|∆Share of labor that uses computers|25.79|48.82|-20.00|0.00|5.00|70.00|100.00|8,333|
|∆Share of labor that uses Internet|27.92|45.97|-10.00|0.00|10.00|70.00|100.00|8,333|
|∆ln(Stock of computer equipment/worker)|-0.17|1.84|-2.46|-0.84|0.12|0.86|1.77|8,333|
|**Employment and wages**|||||||||
|∆ln(Number of white-collar workers)|0.03|0.82|-0.92|-0.41|0.00|0.41|1.10|8,333|
|∆ln(Number of blue-collar workers)|0.01|0.66|-0.69|-0.29|0.00|0.31|0.75|8,333|
|∆ln(Annual real wage white-collar workers)|0.02|0.81|-0.93|-0.42|-0.02|0.47|1.02|8,333|
|∆ln(Annual real wage blue-collar workers)|-0.05|0.68|-0.81|-0.45|-0.06|0.32|0.78|8,333|
|∆White/Blue-collar workers|0.00|0.26|-0.28|-0.10|0.00|0.10|0.29|8,333|
|∆White/Blue-collar wages|0.20|1.63|-1.50|-0.58|0.13|0.97|2.01|8,333|
|**Firm characteristics**|||||||||
|∆ln(Capital/worker)|0.09|1.71|-1.80|-0.78|0.13|0.87|1.90|8,333|
|∆ln(Total employment)|0.04|0.52|-0.51|-0.20|0.00|0.27|0.62|8,333|

_Source:_ Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI 19
we only mention that **E** _sr_ is the _N ×_ 1 vector of exports of country _s_ to country _r_ , **X** _s_ is the _N ×_ 1 vector of gross output produced by country _s_ , **A** is the _GN ×GN_ global matrix of input coefficients, **B** is the global Leontief inverse matrix for the entire inter-country model and **V** _s_ is the 1 _×N_ vector that incorporates the value-added shares embedded in each unit of gross output produced by country _s_ .

In their seminal contribution, Hummels et al. (2001) propose to split gross exports between a share of domestically produced items and a share of imported inputs embedded in exports, where the latter takes into account both the direct and indirect production linkages within the domestic market. Hummels et al. (2001) define the share of import content in exports as the ‘Vertical Specialization Index’ ( **VS** ) which can also be computed for bilateral exports between _s_ and _r_ . In an ICIO framework the **VS** _sr_ indicator can be expressed as:<sup>3</sup> where **u** _N_ is the 1 _×N_ unit row vector and ( **I** _−_ **A** _ss_ )<sup>_−_1</sup> represents the domestic inverse Leontief matrix.

Although the **VS** indicator provides valuable information for several empirical applications,<sup>4</sup> there are relevant aspects that are not taken into account. In particular, imported inputs in exports are considered as a single category, without distinguishing between the part that originated abroad and the part that was originally produced by _s_ itself and then re-imported.

Indeed, by exploiting Inter-Country Input-Output tables, gross exports can be broken down according to the country that initially produced each component. Koopman et al. (2010) use the global Leontief inverse matrix to trace back the total gross output produced by each country _j_ to deliver one unit of country _s_ exports ( **B** _js_ ), and the related value-added shares ( **V** _j_ ). The part that originated in country _s_ is referred to as the ‘domestic content of exports’ ( **DC** _sr_ ), whereas the remaining

> 3Hummels et al. (2001) measures can be computed without resorting to an Inter-Country InputOutput database since they were developed for national Input-Output tables that distinguish only between imported and domestic inputs.

> 4In particular, later in the paper we show that the **VS** indicator is a good measure of the participation of a country in the downstream phases of international production chains. Moreover, the complementary part of the **VS** share of exports is one of the possible measures of domestic value-added embedded in exports (see also Johnson, 2018, on the same point).

6
Fajnzylber, P., Maloney, W., Montes-Rojas G. (2011): “Does formality improve micro-firm performance? Evidence from the Brazilian SIMPLES program”, _Journal of Development Economics_ 94: 262-76.

Fields, G. (2004): “A guide to multisector labor market models”, Retrieved 23 May 2013 from Cornell University, School of Industrial and Labor Relations site: http://digitalcommons.ilr.cornell.edu/workingpapers/86/ Jaramillo, M. (2009): “Is there demand for formality among informal firms? Evidence from microfirms in downtown Lima”, _German Development Institute Discussion Paper_ 12/2009.

Kaplan, D. S., E. Piedra, and E. Seira (2011): “Entry regulation and business start-ups: Evidence from Mexico," _Journal of Public Economics_ , 95(11-12): 1501-1515.

Keizer, K, Lindenberg, S, Steg, L. (2008): “The spreading of disorder”, _Science_ 322(5908): 1681–85.

La Porta, R. and A. Shleifer (2008): “The unofficial economy and economic development”, _Brookings Papers on Economic Activity_ 47(1): 123-35.

Maloney, W. (2004): “Informality Revisited”, _World Development_ 32(7): 1159-78.

McKenzie, D. (2011): “How can we learn whether firm policies are working in Africa? Challenges (and solutions?) for experiments and structural models”, _Journal of African Economics_ , 20(4): 600-25.

McKenzie, D. Sakho, Y.S. (2010): “Does it pay firms to register for taxes? The impact of formality on firm profitability”, _Journal of Development Economics_ 91(1): 15-24.

Mullainathan, S. and P. Schnabl. (2010): “Does Less Market Entry Regulation Generate More Entrepreneurs? Evidence from a Regulatory Reform in Peru,” J.Lerner and A. Schoar, eds., _International Differences in Entrepreneurship_ . Chicago: National Bureau of Economic Research, pp. 159-177.

Wan, J. (2010): “The incentive to declare taxes and tax revenue: The lottery tax receipt experiment in China”, _Review of Development Economics_ 14(3): 611-24.

World Bank (2013): _Doing Business 2013: Smarter Regulations for Small and Medium-Size Enterprises_ .

16
cointegration between _e_ + _wp_ − _pnf_ and terms of trade, but failed to find a stationary vector (not reported). Because of this, and because the measurement of monthly terms of trade is surrounded with some uncertainty, we follow Kool and Tatom (1994) and Garcia-Herrero and Pradhan (1998) and use the Hodrick-Prescott filter to remove the non-stationary component of the real exchange rate (see Appendix for details). We thus assume that the trend obtained is the long-run real equilibrium exchange rate, or that it at least captures the long-run level that is relevant for the adjustment of prices in the goods market.

# _4.3 The Agricultural Sector_

It is not as straightforward to find variables for agricultural output, since only annual data are available. One option is to use the amount of rainfall, as Diouf (2007) does, and another one is to use wholesale prices of agricultural commodities, following Durevall and Ndung’u (2001) and Khan and Schimmelpfennig (2006). We use the annual series for the volume of cereal production, interpolated to monthly observations.<sup>19</sup> Including the growth rate of agricultural production in the models is not a good idea, since it affects income, which in turn affects demand for food. Therefore, the Hodrick-Prescott filter is used to obtain the deviations from the long-run trend in agricultural production. The resulting series can be viewed as a measure of the output gap. It is assumed that demand grows with average agricultural production, and that deviations from this level result in price changes.

Figure 8 shows the output gap and annual inflation from January 1999 to November 2008. The countercyclical pattern is clearly visible, and there is little doubt that variations in agricultural production affected inflation during the study period. It is also evident that other factors influence inflation, particularly since early 2005 when prices continued increasing while output gap remained positive. Moreover, the rapid rise in inflation in 2008 is not fully explained by the output gap. However, our data for 2008 and 2009 might overestimate agricultural production.

> 19 As a robustness check, we experimented with different series, and the choice of series for agricultural output does not matter much. For instance, value added in agriculture gives virtually the same results. Note that the observation for 2007/8 is an official estimate, and the one for 2008/09 is based on satellite information provided by EARS (2008).

24
of the public health system (Alegana et al. 2013). In the data only 12 out of 1,416 health post-month observations are missing in 2013. In addition, 99% of suspected cases were tested parasitologically in the ve districts analyzed. Since both malaria cases and imported cases are calculated based on case data, as long as utilization is relatively uniform across the country, it should not bias results. Based on the DHS data for all the regions, a health facility was visited for fever in children under age 5 in 46% of cases (ANSD and ICF International 2015). The standard deviation of this utilization across regions is 6.5 percentage points. While in the main analysis, I assume uniform utilization, I include a robustness check where cases are scaled by regional utilization in the DHS.

# 2.5 Population Movement Data

The data used to measure short term movement come from phone records made available by Sonatel and Orange in the context of the Data for Development Challenge (Montjoye et al. 2014). The data come from the second phase of the Challenge and consist of 15 billion call and text records for Senegal between January 1, 2013 and December 31, 2013 for all of Sonatel's user base.<sup>14</sup> The data contain information on all calls and texts made or received by a SIM card, their time, date and location of the closest cell phone tower, which enables tracking of SIMs in space as they make calls from dierent tower locations. The data are anonymized, with a random ID provided that makes it possible to track the same SIM over time, but no identifying information on the individuals. On average there are 1,657 calls or texts per ID during the year, and on average an ID has a call or text on 155 days.

Each tower is assigned a health district based on its GPS coordinates. I follow previous literature to assign individuals a daily health district location based on the cell tower of the last call or text of the day (Ruktanonchai et al. 2016). In instances where there are days with no calls, I replicate Wesolowski, Eagle, et al. (2012) and assign the health district location of the day closest to the one missing.<sup>15</sup> A health district location is assigned to each SIM

> 14At this time it was not possible to obtain more recent data or data from other providers.

> 15The appendix includes a robustness check where observations with more than 14 days in a row missing

11
States have adopted the phrase “national suicide” to characterize the current US restrictions on skilled inflows. More broadly, fostering and strengthening skilled immigration is often a prominent pillar in the economic development strategies of countries. In the 2013 World Population Policies report, 40 percent of countries reported policies to raise immigration of high‐ skilled workers, a large increase from 22 percent in 2005.

There are two broad policy approaches used by countries to select high‐skilled individuals, although as we discuss later, most countries display elements of both. The first approach is points or merit based and is often referred to as supply‐side policies to migration, since it focuses on screening of individual applicants for admission. The second conceptual model is an employer‐ driven system. This is also described as a demand‐side approach since it places emphasis on firms selecting skilled workers to admit into the country.

Canada and Australia are prominent examples of countries that implement points‐based systems for skilled migration. These programs select individuals based upon their observable education, language skills, work experience, and existing employment arrangements. Each factor is weighted by a formula and online calculators often help migrants evaluate their characteristics for seeking a skilled work visa.<sup>14</sup> In the Canadian example, migrants need to collect 67 points across six categories. In terms of education, for example, 15 points are awarded for one‐year post‐secondary diploma, trade certificate or apprenticeship, compared to 25 for a doctorate degree. With regards work experience, six or more years of applicable experience receive 15 points, compared to 9 points for just one year of experience.

The points‐based approach offers an explicit statement of migration priorities for public debate thereby generally leading to stable immigration processes over time. Yet it also has certain disadvantages. Establishing and adjusting an optimal weighting scheme over time is hard and multi‐year queues for applicants are possible. The screening process needs to catch migrants who are exaggerating or misrepresenting their qualifications, and there is some scope for adverse selection if those with few other migration options are otherwise attracted. Talented migrants

> 14 For Canada, it is described at <u>http://www.immigration.ca/index.php/en/who‐qualifies‐for‐canadian‐ immigration‐under‐the‐skilled‐worker‐program.</u>

18
Table D.3 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Access to electricity|0.353***|0.362***|0.360|0.353|0.428***|0.433***|
||(0.065)|(0.066)|(0.314)|(0.317)|(0.075)|(0.076)|
|Urban district|0.050|0.047|0.206|0.233|0.120|0.115|
||(0.096)|(0.092)|(0.252)|(0.299)|(0.090)|(0.086)|
|Observations|784|784|132|132|916|916|
|R2|0.49|0.49|0.38|0.38|0.48|0.48|

_Notes_ : The dependent variables are in log, measured in 2017 PPP US$by year. Robust standard errors are clustered at the enumeration area level. *** p _<_ 0.01, ** p _<_ 0.05, and * p _<_ 0.1.
The model presented here makes use of the NWSS household survey, as well as the surveys on water points and water schemes, all of which include geo-locational data.<sup>4</sup> These data present an unprecedented opportunity to use geo-spatial models to analyze, at a detailed level, the geographical characteristics of access to safe water and sanitation across the country.

In sectors outside WASH, many household and facility surveys now include geo-locational information (e.g., the latitude and longitude of survey clusters, recorded via a Global Positioning System [GPS] device at the time of the survey, or linked to spatial administrative boundary data). Spatial, statistical modeling approaches are being developed by exploiting this locational information to generate mapped surfaces of indicators of interest at increasingly fine spatial scales, and with greater precision than was previously possible. Central to many of these approaches is a body of theory known as model-based geo-statistics (MBG) (Diggle & Ribeiro 2007; Diggle et al. 1998). MBG has been successfully applied to point-located survey data to create a wide range of maps, including, for example, mapping malaria prevalence (Gething et al. 2011, 2012) and poverty (World Bank 2016). The availability of the NWSS 2015 data makes it possible to extend the MBG approach to mapping local populations’ access to water and sanitation services, and their proximity to the nearest functioning water source, in Nigeria. The high level of granularity resolved in the mapped outputs can improve our understanding of inequalities in access levels between and within the different regions of the country.

# **2. DATA**

# **National Water and Sanitation Survey (NWSS) 2015**

Data on access to WASH variables come from the 2015 NWSS household survey. The household survey was conducted by the Federal Ministry of Water Resources, which interviewed 201,842 households across 36 states in Nigeria (Figure 1).<sup>5</sup> The survey asked questions relating to

> 4 <mark>All surveyed households and water service points were georeferenced in the surveys to provide latitude and longitude coordinates. Water schemes were also georeferenced using their centroid location, although it should be noted that in many cases these schemes occupy a significant area and so the use of a single central location is a potentially crude approximation of their true spatial extent and coverage.</mark>

> 5 See a more detailed description at Andres et al. (2017).

3
external consultants, though only significant for academia.<sup>21</sup> It does not appear to be the case that the sectors and staff that know less about the Bank’s own research tend to value these external sources more highly.

Why then do we see these differences in familiarity with WB research? It might be argued that they just reflect the nature of the work done by operational staff in different sectors. By this view, staff in the ―hard infrastructure sectors‖ do not need the Bank’s research as much as in the ―social sectors.‖ To paraphrase an argument I have heard: _“We all know that roads are good for development and we don’t need economic analysis to confirm that; what we need is to make sure that the roads meet sound engineering standards.”_ It is hard to say what validity should be attached to this view. There are clearly differences in the nature of the knowledge needed by staff in different sectors. But the need for research is surely common between the ―hard‖ and ―soft‖ sectors. Economic analysis is important in formulating strategies and policies for infrastructure, notably in assessing the effectiveness of lending operations (both _ex ante_ and _ex post_ ) and in devising sound pricing and investment policies. And the trade-offs invariably faced by governments between building roads and schools (say) must be informed by economic analysis. The uneven familiarity with WB research across sectors evident in Table 3 is not easily justified from the point of view of sound development policy and WB lending.

It might be conjectured that the fact that economics is the dominant discipline in Bank research makes that research less relevant to some sectors. As discussed above, this does not seem credible as an explanation for why Bank research is used less in the traditional infrastructure sectors. Nor does it fit well with the fact that staff in Health, Nutrition and Population (HNP)—dominated by non-economists—are very familiar with research (Table 3). Possibly the idea has more salience for the Social Development (SDV) sector, which uses noneconomics social sciences more than economics. (Note that SDV has the third lowest mean familiarity score in Table 3.) This division of labor is not, however, exogenous, but has evolved in a path-dependent way through hiring decisions. And it can change, albeit slowly. For example, DECRG’s latest _Policy Research Report_ (its flagship publication) is a substantial evaluative study, done by two economists, of the main lending instruments used by SDV operations

> 21 The correlation coefficients between individual ratings for familiarity with WB research and academia is

> 0.37 (significant at the 0.1% level) and 0.09 (only significant at the 12% level) respectively.

10