rafmacalaba/datause-encoder-data huggingface dataset:
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE percent of the road fund’s resources are dedicated to the primary network. The goal is to increase road user charges and gradually phase out direct treasury transfers or external funding. But it might be particularly difficult to implement full cost recovery, at 26 cents per liter, given the already high price of fuel.

# Railways

## **Achievements**

Efforts have been made to improve the functioning and capacity of OCBN’s<sup>7</sup> rail system, jointly owned by Benin and Niger, and to bring fresh capital to an investment-starved system. In February 2010, through a competitive process, a concession was granted to a privately owned company. But as OCBN is a joint Niger-Benin venture, Benin requires the agreement and involvement of Niger in any concession agreement—and so far Niger has not agreed to engage.

**Table 7. Railway indicators for OCBN and select other railways**

Latest available year

|Indicator|Unit|OCBN<br>Benin<br>Niger|SITARAIL<br>Burkina Faso<br>Côte d’Ivoire|GRC<br>Ghana|TRANSRAIL<br>Mali<br>Senegal|NRC<br>Nigeria|
|---|---|---|---|---|---|---|
|Concessioned company|1-concessioned, 0-nonconcessioned|1|1|1|1|1|
|Network density|km/km<sup>2</sup>|5.1|2.2|4|5.4|3.8|
|Network density|km/million population|66|39|40|77|23|
|Labor productivity|1,000 traffic units per employee|40|481|84|26|37|
|Carriage productivity|1,000 passenger-km (pkm) per<br>carriage|900|862|416||737|
|Locomotive productivity|Million traffic units per locomotive|3|35|7|40|13|
|Wagon productivity|1,000 net tonne-km (ntkm) per wagon|74|1,020|458|804|59|
|Traffic unit—freight|Million ntkm|24|670|224|409|77|
|Traffic unit—passenger|Million pkm|18|25|64|113|174|
|Freight average yield|US cent/ntkm, average 2000–05|5.8|5.5|4.4|3.3||
|Passenger average yield|US cent/pkm, average 2000–05|2|3.3|2.4|2.2||

_Source_ : AICD railways database.

## **Challenges**

The establishment of OCBN increased the cost of surface transportation in Niger. Benin and Niger jointly operate the Cotonou-to-Parakou railway line and coordinate complementary road transport between Parakou (in Benin) and Niamey (in Niger). In Cotonou, as a rule, shipments are sent by train, but if OCBN cannot accept the shipments within a reasonable period for lack of capacity, a special waver is granted for trucks approved by OCBN. This situation often occurs because the train can only carry between 150,000 and 200,000 tonnes a year on its way from Cotonou, as the theoretical capacity of 600,000 tonnes per year is exceeded. The railroad goes as far as Parakou (see box 1), and then goods and containers are loaded on OCBN-approved trucks based on a rotation system.

> 7 Organisation Commune Bénin Niger.

16
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# **Growth, Globalization, and the Gains from the Uruguay Round**

by Thomas W. Hertel, Christian F. Bach, Betina Dimaranan, and Will Martin Address correspondence to: T.W. Hertel, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, 1145 Krannert Building, West Lafayette, IN 47907-1145. Email: hertel@agecon.purdue.edu.
large firm. Therefore, a rise in market interest rates will lead banks more than capital market lenders.

In a crisis situation, during which the relationship between monetary policy instruments and nominal income changes drastically, assessing the monetary policy stance becomes intricate. Consequently, it may be misleading to focus solely on key indicators of monetary policy when evaluating the monetary policy stance and credit conditions in order to detect "a _credit crunch"._ Evaluation of monetary and credit aggregates must be accompanied with a more detailed investigation of the channels through which firms, banks, and households are affected by changes in monetary policy.

# **Box 2. Illustration of the Relevant Spreads**

The following expression presents the set of interest rate spreads which capture the credit channel effects of the transmission mechanism of monetary policy: SPRo = LR-TB = SPRI + SPR2 where LR = lending rate; TB = Treasury bond rate; SPR1 = corporate bond rate - Treasury bond rate; SPR2 = lending rate - commercial paper rate; The difference between rates on corporate and on Treasury bonds (SPRI) measures the general risk premium as it is perceived by the market. If the balance sheet effect is at work, we expect that this spread will increase after the monetary restriction, reflecting the fact that private sector debt has become relatively riskier vis-a-vis sovereign debt.

The spread between the lending rate and commercial paper (SPR2) is a proxy for the lending chainel effect. The difference between lending rates and commercial paper rates quantifies the premium that bank-dependent borrowers must pay in order to raise external finance relative to those firms able to issue debt on the market. The lending channel effect contends that this spread will increase in the aftennath of the monetary squeeze.

Source: Doma,c and Ferri (1998) As widely acknowledged, a decline in either bank loans or a decline in their growth following a monetary tightening is not sufficient to pin down an adverse movement in banks' loan supply. This is because the decline could be induced either by the corporate sector 8
Figure 3: Expectation of losing health insurance coverage (conditional on had health insurance on the day of the survey interview) <!-- Start of picture text --> 26%<br>14%<br>7%<br>7%<br>2%<br>Cismen Ciswomen Transgender Non-Binary All<br>N=5952.<br><!-- End of picture text --> In relation to the decrease in income and consumption, financial benefits from work or government are among the essential coping strategies that have been adopted to mitigate the economic impacts of COVID-19. Governments across the world have stepped up to help the disadvantaged by widening the scope and reach of fiscal supports (Organisation for Economic Co-operation and Development 2021; International Monetary Fund, n.d.). The data indicates that 23 percent of each group, transgender and non-binary people, reported receiving financial support from work or the government due to the crisis. In comparison, 14 percent of cismen reported receiving financial support (Figure 4).

Nonetheless, the higher rate of transgender and non-binary people receiving financial support is not surprising. They generally are overrepresented in lower-income groups (see Table 2) and experienced elevated expectations of losing jobs and income compared to cismen, and consequently reported a decrease in consumption during the COVID-19 pandemic. The findings from the probit model estimations in Table 3 indicate that, after controlling for individual, household, and regional characteristics, non-binary people are 4 percentage points more likely to report receiving financial benefits from work or government due to the COVID-19 pandemic compared to cismen. However, the monetary support received by 18
(north-west), Kunduz (north-west) and Jalalabad (east) are clearly visible. We include red dots at locations of GTD killings from May 2012-April 2013, demonstrating the nationwide geographic distribution of violence that we exploit in the analysis below.

# **3.2 Measurement Validation**

A key methodological question in this study is how strong is the correlation between activity in the CDR and more traditional measures of firm activity. We complete three different validation exercises using a combination of official government data sources and original survey data.

First, we validate the physical location of firms against CDR measures in Table A2. For each firm appearing in the CDR, we compute the top one, five and ten “modal districts” by first calculating the most commonly used district in all outgoing calls for each subscriber in each month, and then recording the frequency with which each district appears as the mode for all of the firm’s subscribers.<sup>17</sup> In Panel A, we compare these modal districts to the headquarter district locations from two official business registration sources collected in 2016: the Central Business Registry (CBR), where formal firms must register to receive a tax identification number, and the Afghanistan Investment Support Agency (AISA), for firms seeking foreign investment. We successfully name match 934 firms to the CBR dataset and 110 firms to the AISA dataset, and find that 74% (80%) of firm headquarters matched the top modal district and 87% (93%) matched one of the top five modal districts in CBR (AISA). Using original survey data collected from over 400 firms in spring 2017, we complete a second validation exercise in Panel B where we include self-reported districts of headquarters and other offices in either 2014 or 2017. We find that 67% of 2017 office districts match the top 5 modal towers and that 70% match the top ten modal towers, with similar percentages in 2014. In sum, these findings increase our confidence in the potential of CDR data to proxy for the physical locations of employees, especially when using spatially and temporally aggregated

> 17Note that the number of modal districts for a firm is bounded by the number of subscribers. The average (median) firm has 5.8 (2) modal districts.

13
- We scale the carbon input to energy production to match the carbon intensity of GDP: 0.46 kg CO2 per 2015 $ of GDP, averaged over 2000-2022, from World Development Indicators of the World Bank.

- The value of total energy consumption as a share of GDP is 7.1%, calculated from GLORIA database.

- The constant USD price of carbon is restricted to attain primary fossil energy supply expenditure-GDP ratio of 3.9%, from GLORIA database.

- The ratio of total imports to GDP is around 26%, calculated from GLORIA database.

Note that the steady state values of a few important variables have a direct equivalent in the data and have been calibrated accordingly. These values can be set directly and do not require another parameter to be restricted. They are reported in Tables B2 and B3.

# **B.4 Estimation strategy**

In this subsection, we discuss our Bayesian estimation strategy together with the results. The basic idea of Bayesian statistics comes from the Bayes’ rule: where _θ_ is the vector of parameters, _p_ ( _θ|_ **y** ) is the probability distribution of parameters given data (posterior distribution), _p_ ( **y** _|θ_ ) is the probability distribution of data given parameters (likelihood), _P_ ( _θ_ ) is the prior distribution of _θ_ and _p_ ( **y** ) is the marginal distribution of data. We are interested in marginal posterior distribution of model parameters; however it is not always possible to analytically compute marginal posterior distributions due to complex integral calculations.

Bayesians, therefore, have developed numerical methods that allow us to approximate marginal posterior distributions of parameters. The basic idea is to draw from some other distribution (proposal or importance distributions) and find moments of simulated data, which are going to converge to moments of posterior distribution under specific conditions (Herbst and Schorfheide 2015).

We use Metropolis Hastings (MH) algorithm, which is part of MCMC family. MH algorithm simply evaluates each parameter vector drawn ( _θ_<sup>_i_</sup> at iteration i) from a proposal distribution at the kernel of the posterior distribution over the proposal distribution, and compares this to the ratio of the vector stored in iteration (i-1). If the ratio of the former to the latter is higher than a random number u _∈_ (0 _,_ 1), then candidate draw is accepted and stored. If not, _θ_<sup>_i−_1</sup> is stored again among accepted parameter vectors.

There are two important components of the MH algorithm. One is the proposal distribution, which is assumed to be a multivariate normal distribution with mean _θ_<sup>_i−_1</sup> (last 66
introduces the supply chain cost model. Sections 4 through 6 identify the dimensions of supply chain performance that determine total transit costs. Section 7 develops a quantitative model and proposes an application to the Northern Corridor, serving Uganda, Rwanda and Kenya from Mombasa in East Africa. The final section derives the policy implications.

# **Section 2 A Literature Survey and Policy Recommendations**

Variants of the new economic geography, new trade theory, neoclassical and endogenous growth theories have been applied to highlight the nexus between geographic location, trade and economic growth. Some of their conclusions are: (1) landlocked countries trade less vis-à-vis coastal countries<sup>4</sup> (on average 30% less than coastal countries),<sup>5</sup> (2) landlocked countries experience weaker growth than maritime countries<sup>6</sup> (being landlocked reduces average growth by about 1.5 percentage points)<sup>7</sup> and (3) on average landlocked countries had longer recourse to IMF than coastal countries.

The impact of being landlocked is based on the idea of dependence over the transit state. It has produced two main corollaries:

- dependence necessarily implies high transaction costs,

- mitigating measures for landlocked countries result of two set of actions: first, adopting transit rules recognized by the international law, secondly, developing regional transport infrastructure.

Dependence over the transit state necessarily implies high transaction costs (notably transportation costs). High transaction costs are perceived as the result of (i) “transit charges”<sup>8</sup> but also (ii) the difficulties to benefit from regional adequate infrastructure.

> 4 Irwin and Tervio (2002).

> 5 Limao and Venables (2001).

> 6 Bloom and Williamson (1997).

> 7 MacKellar _et al_ (2002).

> 8 UNCTAD (2002) gives the example of port charges, road tolls, forwarding fees, customs bonds or transport quota restrictions. Further details and examples are given in section 4.

5
Second, data from the EBRD also suggests that while the large-scale privatization program has effectively stalled, the private sector share of output has nonetheless grown steadily. While this observation is consistent with, say, the growing efficiency of existing private sector firms in producing output, it is also fully consistent with growing factor inputs (labor) among private firms, including the informal sector, and falling employment in state enterprises.

Third, there are some data on sector-by-sector employment in the RS, disaggregated by enterprise ownership. Unfortunately, the forms of ownership reported include those of mixed private and public ownership. Depending on whether or not mixed forms of ownership are included, SOE employment in RS accounted for about 15 to 40 percent of total (formal sector) employment in late-2003. According to the LSMS, SOE employment currently accounts for 30 percent of formal sector employment. Regardless of how SOE employment is calculated, averages drawn from semi-annual data available suggest that SOE employment in the RS has steadily fallen in recent years as private sector employment has grown.<sup>14</sup> Fourth, summary data from the South-Eastern Europe Barometer (SEB) suggest that employment in state enterprises ranges from 16 to 24 percent, based on survey data collected in 2004. Available summary data do not allow us to calculate the weighted average of SOE employment for BH as a whole but the average drawn from the LSMS falls within the SEB estimates.

These three data sources do not—separately, on their own—provide conclusive evidence that SOE employment has indeed fallen as a share of total employment but they do provide strong evidence that is broadly consistent with the LSMS findings.

_SOE Evolution and Poverty Outcomes_ What are the poverty and welfare outcomes for those who left the SOE sector and those who stayed?<sup>15</sup> On the one hand, those who left were likely to experience more severe wage arrears in 2001 and are now substantially less likely to experience wage delays. On the other hand, those who left seem more likely to be poor. The poverty headcount of those who left is about 20 percent, compared to 15 percent poverty rate for those who stayed.

This puzzling result—the poverty outcomes of those who left the SOE sector being higher than those who stayed behind—seems driven largely by the poverty risk associated with the subsequent labor force status and sector of employment of those who left. Of the workers who were employed in the SOE sector in 2001 but subsequently left about 40 percent found employment in the formal sector (mostly in manufacturing) in 2004 (Figure 10). The rest either became unemployed or left the labor force, or found

> 14 See RS Institute of Statistics (various issues).

> 15 Due to the relatively few responses to the relevant survey question, the LSMS survey cannot effectively distinguish the reasons for leaving, whether it is due to dismissal, for example, or voluntary exit.

20
more cheaply due to automation; and secondly, higher overall income driving higher demand for all goods. Automation can increase aggregate employment if the latter effects are large enough.

This section shows that aggregate job gains can result from labor-substituting technologies, but likely alongside an even larger fall in employment shares in the sector most affected historically, industry. The finding applies where low price elasticities of substitution and low income elasticities of demand exist for those sectors subject to automation. Where there is little or no offsetting income or substitution effect from automation affecting the industrial sector, aggregate employment will rise by less or fall by more, but employment shares in industry will remain more robust. This framework purely considers demand-side mechanisms, while there are a range of supply-side factors that could contribute to this finding, complementing this approach.

This framework augments that of Acemoglu and Restrepo (2018, 2020) by also considering the effects of different income-elasticities of demand between sectors; it is well documented that the demand for industrial goods falls relative to that of services as income rises (Rodrik, 2016; Comin et al., 2015). This helps to explain why the industrial sector shrinks more in economies with large positive income and employment effects from automation, but less so in economies that have not experienced as large a positive effect.

In a simplification, a model is created of an industrial sector that is subject to labor-substituting technological change, and a services sector that is not. While it is true that certain jobs in the services sector have been automated, analysis of U.S. and European job markets has found that industrial middle and low-skilled jobs have been most affected in recent decades by automation (Goos et al., 2014; Autor, 2015).

**Firms** are split into sectors _k_ that produce industrial goods (I), and those that produce nonautomatable services (NAS). Industrial goods and services producers produce output _X_ ( _k_ ) by combining tasks _x_ , such that production is performed as

Tasks, _S_ , are divided into those that can only be performed by workers ( _w_ ), and those that can be performed by labor or capital ( _r_ ). Specifically, in the industrial sector tasks _S ∈_ [0 _, M_ ] are technologically automated and can be performed by capital. The output of capital in every task is set to 1, and to further simplify the model it is assumed that the productivity of labor in each task is constant as well and equal to _γ_ . Consequently, the production function for task _s_ in industry _k_ can be written as: There is therefore perfect substitution of labor for capital in the automatable tasks _s < M_ . It 25
owned resources to the non-farm sector as well adjusting the output composition to changes in relative land returns. Total farm household income in the model is then determined as the sum of returns on their endowments employed in agriculture, plus the returns on those employed in non-agriculture.

While the average farm household’s welfare change is an important component in assessing WTO outcomes for any given country, greater detail on the distribution around this average is required to develop insight into the political economy of agricultural reform. This requires more disaggregate data. We have obtained these data for the US, and we use a “micro-simulation” technique that uses the general equilibrium changes in product and factor prices to evaluate the welfare impact on different groups of farm households in this country. These different groups are defined first by their product specialization and then by their place in the wealth distribution of similarly specialized producers. The households and their initial income sourcing are benchmarked using the ARMS annual survey data of the United States farm household population for 2004. The ARMS survey data has no longitudinal component, and Hill (1996) argues that in such cases wealth provides a suitable substitute for multi-period averages necessary to accurately gauge the income position of farm households.

Table 1 identifies the disaggregate US farm households of our study. They represent income specialized households in four highly protected sub-sectors: dairy, cotton, rice and sugar, and a residual category of non-specialized farm households. The specialization criterion is that at least 1/3 of farm revenue be derived from rice, cotton, or dairy (to be specialized in those products), and 1/5 of farm revenue from sugar (to be specialized in sugar). The second line of delineation among households distinguishes 12