rafmacalaba/datause-encoder-data huggingface dataset:
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1 Infrastructure has contributed much to economic growth—but could contribute much more**

Infrastructure‘s contribution to annual per capita economic growth in selected countries, 2003–07, in percentage points <!-- Start of picture text --> telecom electricity roads<br><!-- End of picture text --> Potential contributions of infrastructure to annual per capita economic growth in selected countries, in percentage points <!-- Start of picture text --> telecom electricity roads<br><!-- End of picture text --> Source: Calderon 2009.

# The state of Côte d’Ivoire’s infrastructure

Côte d’Ivoire’s population is concentrated in the southwest and southeast of the country. Almost a quarter of the population lives in the economic capital city of Abidjan (figure 2a). However, the bulk of the mining activity is located in the central and northern part of the country (figure 2b). The northern area is sparsely populated, and nearly 4 out 5 persons living there were below the poverty line in 2008 (Government of Côte d’Ivoire PRSP 2008).

3
- 4 -

# **II. RECENT DEVELOPMENTS IN ENTERPRISES AND TRANSPORT INFRASTRUCTURE**

6. In Mozambique, the manufacturing sector is generally thin, while some agribusinesses exist. As of 2016, there were about 58,000 firms registered ( **Figure 2** ). Since 2014, the formal business sector has been growing rapidly. Historically, the firm distribution is highly concentrated in major urban areas. The City of Maputo and Maputo Province account for about half of the total enterprises, followed by Gaza, Manica and Niassa ( **Figure 3** ). The northern provinces generally have a fewer enterprises, meaning that few job opportunities are available to people living in those areas, except for agriculture.

7. As far as “new firms” are concerned,<sup>1</sup> the 2014/15 national firm census data indicate that the traditional major cities experienced some reduction in the number of firms during the early 2010s. New firms seem to have been emerging in some nontraditional areas, such as Sofala, Nampula and Tete Provinces ( **Figure 4** ).<sup>2</sup> Although these changes are still marginal compared with the existing business base of the traditional urban areas, it can be interpreted to mean that some firms do not prefer to be located in traditional areas, such as Maputo, possibly because of increasing congestion and high business costs. Non-traditional areas may have become more attractive because of enhanced regional integration with neighboring countries.

8. Different industries are located in different places, depending on their nature and requirements. For instance, agribusiness, which is one of the promising industries in Mozambique, is concentrated in agricultural production areas ( **Figure 5** ). Manica and Sofala Provinces are considered to have the great advantage in packaging fruits and vegetables, meat and dairy products, and processing of seafood and rice (Ministry of Industry and

> 1 With available data, it is defined by a net increase (or decrease) in the number of firms over time. No information is available in gross terms.

> 2 There is a possibility that this may be caused by statistical errors in the database, which is by no means perfect, with many informal firms excluded.
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led to an inflation upsurge in 1992 (Figure 3c). Interestingly, although the conflict lasted until 2002, the second half of the 1990s saw annual inflation plummet in Algeria, going from 30% to less than 3% in just five years.

<!-- Start of picture text --> Figure 3a. Algeria and Tunisia ‐ Public Debt (% of GDP)<br>120<br>100<br>80<br>60<br>40<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Algeria Tunisia Military coup Onset<br>Figure 3b. Algeria and Tunisia ‐ Share of Government Consumption<br>(as % of GDP, in PPPs)<br>30<br>24<br>18<br>12<br>6<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Algeria Tunisia Military coup Onset<br>Public debt (as % of GDP)<br>(as % of GDP, in PPPs)<br>Share of Gov. Consumption<br><!-- End of picture text --> 11
8 Parameter estimates from all the first-stage models are then taken, in the second stage, to the population census. Since predicted household-level per capita consumption in the census is a function not only of the parameter estimates from the first stage consumption models estimated in the survey, but also of the precision of these estimates and of those parameters describing the disturbance terms in the consumption models, we do not produce just one predicted consumption level per household in the census. Rather, _r_ predicted expenditures are simulated for each household (typically around 100 simulations). The full set of simulated household-level per capita expenditures are then used to calculate estimates of the welfare estimates of each target population. Demombynes, Elbers, Lanjouw and Lanjouw (2007) describe a variety of simulation approaches that are available and document that these all yield closely similar welfare estimates. Validation studies of the poverty mapping methodology remain rare; in settings where one can rigorously check the method, it is likely that it was not needed in the first place. However, a few such studies have been conducted and have yielded encouraging findings (see for example Demombynes, et al, 2007, and Elbers et al, 2008). We examine in Section 7 whether there are grounds for suspecting that our broad findings concerning the relationship between urban poverty and city size are due to our employment of small-area estimates of poverty as opposed to direct measures.

# **3. Data**

Poverty-mapping exercises based on the methodology just described have now been conducted in a number of countries. We have selected eight of these countries, on the basis of the availability of the micro-data files and of regional coverage, for analysis in this paper. Table 1 lists the total urban population (at the Census year) in the eight countries, both in absolute numbers and as a share of the total. The sample includes a wide variety of countries, from the relatively small (e.g. Albania) to the relatively large (e.g. Brazil), and from the predominantly rural (e.g. Sri Lanka) to the highly urbanized (e.g. Brazil). The table also indicates which household survey was used for the estimation of the household expenditure model, including year and sample size. The year of the
# **Table 1:  Summary statistics**

|**Variables**|**N**|**Mean**|**S.D.**|**N**|**Mean**|**S.D.**|
|---|---|---|---|---|---|---|
|||**Original sa**|**mple**|**Supp**|**lementary **|**sample**|
|Fish in 07|348|0.583|0.494|557|0.734|0.442|
|Fish in 09|348|0.580|0.494|557|0.661|0.474|
|Report catch in 07|348|0.644|0.480|557|0.806|0.396|
|Boat aid in 07|348|0.371|0.484|557|0.587|0.493|
|Boat aid in 09||||557|0.724|0.448|
|Buy in 07|348|0.144|0.351|557|0.120|0.326|
|Buy in 09||||557|0.298|0.458|
|Boat owner in 07|348|0.511|0.501|557|0.716|0.451|
|Boat owner in 09||||557|0.488|0.500|
|**AGGSHOCK**|||||||
|Fraction of bad aid boats in 07|313|0.247|0.384||||
|Bad0 1/|313|0.617|0.487||||
|Bad50 1/|313|0.227|0.419||||
|**IDIOSHOCK**|||||||
|D_have a bad aid boat in 07|348|0.006|0.076|557|0.097|0.296|
|D_have a bad aid boat in 09||||557|0.190|0.393|
|D_bad house in 07|348|0.382|0.486|557|0.409|0.492|
|D_bad house in 09||||557|0.510|0.500|
|D_have leaky roof in 09||||557|0.255|0.436|
|D_have cracked wall in 09||||557|0.329|0.470|
|D_have poor foundation in 09||||557|0.221|0.415|
|<br>D_have faulty plumbing in 09||||557|0.246|0.431|
|**Outside option**|||||||
|Number of NGOs in 07|329|15.833|7.267||||
|Number of aid projects in 07|329|37.246|18.280||||
|Total housing aid in 07 2/|331|252.752|271.663||||
|**Fishing productivity**|||||||
|Ln (boat length pre-tsunami) 3/|320|2.159|0.226||||
|Ln (engine capacity pre-tsunami) 3/|326|3.061|0.479||||
|Total fish catch/length pre-tsunami 3/|326|26.319|38.056||||
|In-shore fishing skill|348|0.348|0.476||||
|**Socio-economic characteristics**|||||||
|Boat owner and captain pre-tsunami|348|0.736|0.442|557|0.610|0.488|
|D_large boat pre-tsunami|348|0.330|0.471|557|0.305|0.461|
|High education pre-tsunami|341|0.141|0.348||||
|D_debt to Toke Bangku|348|0.201|0.401||||
|High income in 07||||556|0.252|0.434|
|**Community characteristics**|||||||
|Number of boat aid in 07|313|18.217|20.132||||
|Number of surviving captains in 05|309|23.379|19.561||||
|D_Banda Aceh|348|0.175|0.381|557|0.158|0.365|
|D_Aceh Jaya||||557|0.140|0.347|
|Fraction of fishing population in 07||||491|0.271|0.270|
|Credit union in village in 07||||522|0.318|0.466|

1/ Bad0=1[Fraction of bad aid boats=0], Bad50=1[Fraction of bad aid boats>0.5] 2/ Both temporary and permanent houses.

3/ Observations above the top 5 percentile are trimmed out.
Bill in the U.S. and the 1992 Common Agricultural Policy reform in the EU), they typically apply to all commodities of the respective programs (Baffes and De Gorter 2005).

# **7. Supercycles**

The permanent component of most prices follows a U-shaped pattern which captures the end and the beginning of the two post-WWII supercycles (see Figures B1-B27 of Appendix B). The trough, which occurs in the late 1990s and early 2000s, is consistent with the beginning of the last supercycle triggered by a surge in EMDE demand. To examine the contribution of supercycles to permanent shocks, we extended the analysis by using annual data during the 1900-2020 period.<sup>**14**</sup> The decomposition was applied to a subset of commodities (15 instead of 27) by using the same criteria applied to the earlier analysis. Hence, we excluded energy (because for most of the pre-1970 period energy prices, especially oil, were negotiated and changed infrequently), fertilizers (because they were not important during the first half of the sample), and precious metals (because for most of the pre-WWI period, their prices did not vary).<sup>**15**</sup> Summary statistics from the 1900-2020 decomposition analysis are presented in Table 5. The second column reports the share of the permanent component based on annual frequency during 1970-2020 and compares it with the monthly frequency (shown in the first column of Table 5, which is the same as the share reported in Table 3). The comparison confirms that, while the results from the two frequencies are remarkably similar, changing the data frequency from monthly to annual increases marginally the share of the permanent component at the expense of the business cycle and short term fluctuations, as expected. Results based on the longer sample are reported in the last three columns of Table 5: Permanent component (column III), supercycle (column IV), and long term trend (column V). A summary of the results is also depicted in Figure 7. A number of important findings emerge from the supercycle analysis. First, the decomposition revealed four supercycles, consistent with the literature summarized in Table 2 (also shown in Figure 8). Second, the share of the permanent component based on the 1900-2020 sample was much larger compared to the 1970-2020 sample for all commodities, in some cases considerably so—the average share increased from 60 percent to 80 percent. Third, longterm trends (with frequency of more than 50 years) account for twice as much price variability compared to supercycles (with frequency between 20 and 50 years). Fourth, metals revealed a high degree of heterogeneity while annual agriculture is a highly homogenous group, further confirming results from the earlier analysis. Lastly, while the permanent component of agriculture was dominated by long-term trends, that of metals was

> **14** The sample based on 1970-2020 data is not long enough to capture supercycles which could last up to 50 years. Thus, the sample extension to 1900-2020.

> **15** The annual data used for analysis came from the World Bank’s database (since 1960), complemented by Grilli and Yang (1988) during 1900-60.

_— 13 —_
The school fixed-effects model relies on the variation in subject-matter knowledge between teachers across different classrooms. And as endogenous matching within schools seems unlikely to be happening at scale, the statistically significant results we find in the results section (4) indicate that teachers with more subject-matter knowledge are indeed better teachers. While this finding is important in its own right, it does not imply that teachers with more subject-matter knowledge are better _because_ they have more knowledge. It could be, for example, that teachers who score higher on subject-matter tests tend to be more motivated, and that it is the additional motivation that makes them perform better. For the purpose of policymaking it is important to establish the causal effect of additional subject-matter knowledge, by isolating the subject-matter knowledge effect from other factors such as motivation and other (correlated) skills.<sup>13</sup> In an attempt to do this, we estimate a **student-teacher fixed-effects model** . The idea of the model is to only rely on variation in test scores for a single student-teacher pair, across three different subjects: math, science and Indonesian language. If a teacher’s subject-matter knowledge were truly important we would observe that students tend to do better in one subject (say math) than in another subject (say Indonesian language) if his or her teacher is better in math than languages. The idea of using variation across different subjects in an attempt to isolate the causal effects of teachers’ subject-matter knowledge on student learning is not new. Metzler and Woessman (2012) already used the idea on Peruvian data and find that a 1.0 standard deviation increase in the subject-matter knowledge of teachers leads to a 0.10 standard deviation increase in student learning. Metzler and Woessman (2012) however estimate their model on a cross section of data only.

# **3.2 Measurement error in test scores: From true achievement to observed test scores**

The _x_<sup>_∗_</sup> ’s and _y_<sup>_∗_</sup> ’s in the model are so-called true achievement scores. “True” refers to the idea that they are measured precisely, i.e. without error. In real life, observed test scores measure

> 13If the subject-matter knowledge scores used in the analysis only proxy for differences in the level of motivation, government programs to improve the levels of subject-matter knowledge of teachers would not be effective as they do not change levels of motivation.

15
39

# **Box 8 Nontariff Measures Facing Egypt's Major Exports**

Compared to the situation facing developing countries in total, Egypt's exports do not appear to be more affected by OECD nontariff measures. As shown below, 17.6 percent of all shipments to the USA face NTMs while the coverage ratio is about 4 points lower in the EU. No NTMs have been reported on Egypt's exports to Japan. Separate tabulations (Low and Yeats, 1994) indicate that about 18 percent of all developing countries' exports to the OECD encounter nontariff measures.

||1992 Impo|rts from Egyp|t ($million)|Egypt's N|TM Coverage|Ratios (%)|
|---|---|---|---|---|---|---|
|Product Group*|EU|Japan|USA|EU|Japan|USA|
|All Items|2,893.4|91.0|465.6|13.2|0.0|17.6|
|All Foods and Feeds|114.6|0.4|6.2|19.6|0.0|0.0|
|Food and Live Animals|111.3|0.4|5.9|19.8|0.0|0.0|
|Beverages and Tobacco|0.2|--|0.3|47.4|0.0|0.0|
|Animal and Vegetable Oils|--|0.0|--|0.0|--|--|
|Agricultural Materials|26.5|21.9|6.2|0.0|0.0|0.3|
|Ores & Nonferrous Metals|126.6|2.0|0.3|0.0|0.0|0.0|
|Mineral Fuels|1.969.5|57.0|242.4|0.0|0.0|0.0|
|Manufactures|649.2|9.1|191.7|69.5|0.0|40.2|
|Chemicals|72.0|0.1|2.2|0.0|0.0|0.0|
|Textiles|168.9|2.5|42.5|98.6|0.0|87.0|
|Transport & Machinery|245.1|--|1.7|0.0|0.0|0.0|
|Clothing|83.1|0.1|122.7|95.5|0.0|34.3|
|Miscellaneous Manufactures|6.9|0.4|14.5|0.0|0.0|0.0|

*See Box 6 for the SITC numbers of the product groups listed below.

Well above average coverage ratios occur for Egypt's textile and clothing exports to the EU and USA, and for foodstuffs exported to the European Union. European MFA restrictions are applied to almost all of Egypt's textile and clothing exports with quotas on cotton yarns primarily responsible for the 98 percent coverage ratio for SITC 65.

Foodstuffs exported to the EU also have relatively high NTM coverage ratios due to the application of two or three different types of measures. For example, Egypt's exports of cane molasses (its largest food export with over $9 million traded) face variable import levies while Globe Artichokes and fresh oranges encounter reference import prices. Tariff quotas are applied to most EU bovine meat imports while quotas are applied to coffee and coffee based food preparation.
# **References**

Ayers, J.C., Goodbred, S., George, G., Fry, D., Benneyworth, L., Hornberger, G., Roy, K., Karim, M.R., Akter, F. (2016) Sources of salinity and arsenic in groundwater in southwest Bangladesh. Geochemical Transactions 17, 1-22.

- BBS, (2015) Population and housing census 2011, Socio-economic and demographic report, National Report, vol. 4. Bangladesh Bureau of Statistics (BBS) Dhaka.

- BBS and UNICEF, (2010) Multiple Indicator Cluster Survey (MICS) 2009, Volume I: Technical Report, Progotir Pathey. Bangladesh Bureau of Statistics (BBS) and UNICEF Bangladesh.

BBS/WFP/IFAD, (2012) Undernutrition Maps of Bangladesh 2012. Bangladesh Bureau of Statistics (BBS), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), Dhaka, Bangladesh, p. 24.

- BGS and DPHE, (2001) Arsenic Contamination of Groundwater in Bangladesh, Vol. 2. Final Report  WC/00/19, in: Kinniburgh, D.G., Smedley, P.L. (Eds.), National Hydrochemical Survey. British Geological Survey (BGS) and Bangladesh Department of Public Health Engineering (DPHE), Keyworth.

- Bhattacharya, P., Hossain, M., Rahman, S.N., Robinson, C., Nath, B., Rahman, M., Islam, M.M., Brömssen, M.V., Ahmed, K.M., Jacks, G., Chowdhury, D., Rahman, M., Jakariya, M., Persson, L.Å., Vahter, M. (2011) Temporal and seasonal variability of arsenic in drinking water wells in Matlab, southeastern Bangladesh: A preliminary evaluation on the basis of a 4 year study. Journal of Environmental Science and Health, Part A 46, 1177-1184.

- Burgess, W.G., Hoque, M.A., Michael, H.A., Voss, C.I., Breit, G.N., Ahmed, K.M. (2010) Vulnerability of deep groundwater in the Bengal Aquifer System to contamination by arsenic. Nature Geoscience 3, 83-87.

- BWDB, (2013) Hydrogeological study and mathematical modelling to identify sites for installation of observation well nest, Final Report “Establishment of monitoring network and mathematical model study to assess salinity intrusion in groundwater in coastal area of Bangladesh due to climate change". Bangladesh Water Development Board (BWDB) and Institute of Water Modelling (IWM), Dhaka.

- Conti, K.I., Velis, M., Antoniou, A., Nijsten, G.-J., (2016) Groundwater in the Context of the Sustainable Development Goals: Fundamental Policy Considerations. International Groundwater Resources Assessment Centre (IGRAC), Delft.

- Dhar, R.K., Zheng, Y., Stute, M., van Geen, A., Cheng, Z., Shanewaz, M., Shamsudduha, M., Hoque, M.A., Rahman, M.W., Ahmed, K.M. (2008) Temporal variability of groundwater chemistry in shallow and deep aquifers of Araihazar, Bangladesh. Journal of Contaminant Hydrology 99, 97-111.

- DPHE, (2016) Circlewise watre source status and coverage - June 2016. Department of Public Health Engineering (DPHE), Dhaka, Bangladesh.

- DPHE/JICA, (2010) Situation Analysis of Arsenic Mitigation 2009. Department of Public Health Engineering, Japan International Cooperation Agency Bangladesh, Dhaka.

- EPA, (2018) 2018 Edition of the Drinking Water Standards and Health Advisories, EPA 822F-18-001. U.S. Environmental Protection Agency, Washington, DC.

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Table 2: Price elasticities of microfinance demand and borrower surplus

|(Karla|n & Zin|man, 2019)|
|---|---|---|
|Year 1|Year 2|Year 3|
|(1)|(2)|(3)|
|**Panel A: Regression estimates of demand elastici**|**ties**||
|log loan|amount|(’000 MXN)|
|Intercept<br>8.61|8.95|9.16|
|(0.04)|(0.05)|(0.06)|
|[0.000]|[0.000]|[0.000]|
|log loan amount,<br>0.72|0.53|0.43|
|Year 0, demeaned<br>(0.04)|(0.04)|(0.05)|
|[0.000]|[0.000]|[0.000]|
|log price (_εt_)<br>6.40|10.50|14.08|
|(3.22)|(4.30)|(5.25)|
|[0.047]|[0.015]|[0.007]|
|# of regions<br>78|78|78|
|# of region-month observations<br>936|936|390|

**Panel B: Estimates of compensated demand elasticities and consumer surplus**

|Compensated loan amount elasticity (_φt_+_εt_)<br>(Lower bound)|4.40|8.25|11.70|
|---|---|---|---|
|Compensated loan amount elasticity (_φt_+_εt_)<br>(Upper bound)|6.90|11.25|14.95|
|_Surplus from low interest rate treatment_<br>_per $100 control group loan amount_||||
|Marshallian surplus|$2.11<br>($0.07)|$2.20<br>($0.10)|$2.28<br>($0.12)|
|Equivalent variation (Lower bound)|$2.07|$2.15|$2.23|
|Equivalent variation (Upper bound)|$2.12|$2.22|$2.30|
|_Surplus from microfnance_<br>_per $100 loan amount_||||
|Marshallian surplus|$15.62<br>($7.84)|$9.53<br>($3.90)|$7.10<br>($2.65)|
|Equivalent variation (Lower bound)|$14.48|$8.89|$6.69|
|Equivalent variation (Upper bound)|$22.71|$12.13|$8.55|

_Notes:_ Regression coefficients and consumer surplus are presented in this table, with standard errors in parentheses and p-values in brackets. In Panel A, regression coefficients are presented; Columns 1 through 3 are estimated by OLS with robust standard errors clustered at the region-level. In Panel B, bounds on compensated loan amount elasticities and consumer surplus are reported.

34