rafmacalaba/datause-encoder-data huggingface dataset:
A follow-up survey took place between November 2013 and June 2014. The survey teams returned to the same villages sampled for the baseline survey and identified (i) the women interviewed at baseline and (ii) women with the most recent birth or pregnancy in each village.<sup>5</sup> Interviews were completed with 2,157 of the baseline women and 2,343 women with recent pregnancies. Tracking of baseline women was attempted also outside the original districts in which they resided, which was the main driver of the longer duration of fieldwork. 2,200 CHWs in charge of maternal and neonatal health in the same villages were interviewed as well as 197 presidents of CHW cooperatives.

The outcome measures are constructed using data from the household, CHW and cooperative surveys. The household surveys are used to construct measures of health service utilization, use of modern family planning methods, fertility and interactions between women and CHWs. Data from the CHW survey are used to construct measures of CHW behavior, motivation and satisfaction. Lastly, data collected through the interviews with cooperative presidents are used to construct measures of cooperative dynamics.

# **Descriptive Analysis**

Tables 2-4 present summary statistics of the baseline characteristics of the samples of women with recent pregnancies, CHWs in charge of maternal and neonatal health and CHW cooperatives. The tables report means by exposure to the CHW cooperative performance incentives treatment.<sup>6</sup> In addition, the tables report p-values of t-tests assessing the similarity between treatment and control groups. Overall, the randomization achieved balance between the two groups with respect to observable characteristics.

About 19 percent of the households are from the South Province, 28 percent from each of the East and West Provinces, and 25 percent from the North province. Households consisted of an average of five members and were located at an average distance of about 4.3 kilometers from the health center serving their village. The average age of the sample of women with recent pregnancy was 28.2 and 91 percent were married. Eighteen percent of the women have never attended school, 70 percent attended at least some primary school, and the rest have attended at least some secondary or higher

> 5 The same procedure as in the baseline survey was implemented to identify the women with recent pregnancies. 6 The two study arms in which CHW cooperatives were paid for performance are combined to define the treatment group for the purpose of this paper.

8
**Figure 2: Upward Mobility by Ethnicity**

# (a) Better educated than father

<!-- Start of picture text --> Hill Brah. Hill Chhe. Terai B&MC Dalit Newar Hill Jana. Terai Jana. Musl.& Oth.<br>.8<br>.7<br>.6<br>.5<br>Predicted probability<br>.4<br>.3<br><!-- End of picture text -->

# (b) Better occupational status than father

<!-- Start of picture text --> Hill Brah. Hill Chhe. Terai B&MC Dalit Newar Hill Jana. Terai Jana. Musl.& Oth.<br>.8<br>.6<br>.4<br>Predicted probability<br>.2<br>0<br><!-- End of picture text --> _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014 _Notes:_ Ethnicity categories are hill Brahmin, hill Chhettri, Terai Brahmin and Middle Castes, Dalit, Newar, Hill Janajati, Terai Janajati, Muslim and Others.

5
zero amongst the poorest 20 or so countries, where absolute poverty clearly dominates. The economic gradient in national poverty lines evident in Figure 1 is driven more by the gradient in the non-food component of the poverty lines (which accounts for about 60% of the overall elasticity) than the food component, although there is still an appreciable share attributable to the gradient in food poverty lines (RCS).

Our international poverty line is $1.25 a day for 2005, which is the mean of the lines found in a reference group of countries defined as those with consumption per capita at 2005 PPP below $60.00 per month; the RCS sample has 15 countries in this group—namely Malawi, Mali, Ethiopia, Sierra Leone, Niger, Uganda, Gambia, Rwanda, Guinea-Bissau, Tanzania, Tajikistan, Mozambique, Chad, Nepal and Ghana. (Their median poverty line is $1.27 per day.) Consumption per capita for this group ranges from $1.03 to $1.87 per day with a mean of $1.40 per day. The level of this poverty line is quite robust to the choice of the poorest 15 countries (taking plus or minus five countries ranked by consumption per capita). However, it makes sense to focus on the poorest 15 since the econometric tests reported in RCS imply that national poverty lines tend to rise with consumption per person when it exceeds about $2 per day, which is near the upper bound of the consumption levels found amongst these 15 countries.<sup>14</sup> Of course, there is still a variance in the national poverty lines at any given level of mean consumption, including amongst the poorest countries. The poverty lines found amongst the poorest 15 countries vary from $0.70 to $1.90 per day and RCS estimate the robust standard error of the $1.25 line to be $0.10 per day.

To assess the robustness of qualitative comparisons to the choice of poverty line, we provide estimates for five lines at 2005 PPP namely: (i) $1.00 a day, which is very close to the national poverty line used by the Government of India;15 (ii) $1.25, which is the mean poverty line for the poorest 15 countries, as proposed by RCS; (iii) $1.45, as obtained by updating the 1993 $1.08 line for inflation in the US; (iii) $2.00, which is the median of the RCS sample of national poverty lines for developing and transition economies and is also approximately the line

> 14 RCS use a suitably constrained version of Hansen’s (2002) method for estimating a piece-wise linear (“threshold”) model. (The constraint is that the slope of the lower linear segment must be zero and there is no potential discontinuity at the threshold.) This method gave an absolute poverty line of $1.23 (t=6.36) and a threshold level of consumption (above which the poverty line rises linearly) very close to the $60 per month figure used to define the reference group.

> 15 India’s official poverty lines for 2004/05 were Rs 17.71 and Rs 11.71 per day for urban and rural areas. Using our urban and rural PPPs for 2005 (described below) these represent $1.03 per day.

10
ILO (2018). Women and Men in the Informal Economy: A Statistical Picture. Third edition. Geneva: International Labor Office, International Labor Organization (ILO).

<mark>Islam, A., Palacios Lopez, A., & Amin, M. (2019). Decomposing the Labour Productivity Gap between Migrant-Owned and Native-Owned Firms in Sub-Saharan Africa.</mark> _<mark>Journal of Development Studies</mark>_ <mark>, 55(9): 2065-2082.</mark> Klepper, S. 1996. “Entry, Exit and Innovation over the Product Life Cycle,” _American Economic Review,_ 86(3), 562-583.

Klevorick, A., Levin, R., Nelson, R., and Winter, S. (1995). On the sources and significance of inter-industry differences in technological opportunities. Research Policy, 24: 185–205.

Koellinger, P., 2008. Why are some entrepreneurs more innovative than others? Small Business Economics 31(1): 21–37.

La Porta, R., & Shleifer, A. **(** 2008). The Unofficial Economy and Economic Development. _Brookings Papers on Economic Activity_ Fall: 275 – 352.

La Porta, R., and Shleifer, A. (2014). Informality and Development. _Journal of Economic Perspectives_ , 28 (3): 109 – 126.

Leiponen A (2005) Skills and Innovation. Int J Ind Organ 23(5–6):303–323. https://doi.org/10.1016/j.ijindorg.2005.03.005 Levin, R.C., Cohen, W.M., Mowery, D.C. (1985). R&D, appropriability, opportunity and market structure: New evidence on some Schumpeterian hypotheses. American Economic Review. Papers and Proceedings 75(2): 20-24.

Lileeva, Alla and Daniel Trefler, “Improved access to foreign markets raises plant level productivity... for some plants,” The Quarterly journal of economics, 2010, 125 (3), 1051–1099.

Loayza, N. (1996). The Economics of the Informal Sector. A Simple Model and Some Empirical Evidence from Latin America. _Carnegie-Rochester Conference Series on Public Policy_ , 45: 129162.

Mairesse, J. and P. Mohnen (2002), “Accounting for Innovation and Measuring Innovativeness: An Illustrative Framework and an Application, _American Economic Review_ , **92(2)** , 226-230.

Maloney, W. F. (2004). Informality Revisited. _World Development_ , 32 (7): 1159 – 1178.

<mark>Martin Srholec, 2011. "A</mark> <u>multilevel analysis of innovation in developing countries ," Industrial and Corporate Chang</u> <u><mark>e, Oxford University Press, vol. 20(6), pages 1539-1569, December</mark></u> Maskus, K.E., Neumann, R. and Seidel, T. (2012) How national and international financial development affect industrial R&D. _European Economic Review_ 56: 72-83.

39
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Only a few countries in ECOWAS have made progress toward achieving international standards in air safety, making this area ripe for further regional collaboration. In some cases, such as Nigeria, problems with air transport safety have been an undesirable consequence of market liberalization. A number of countries (including Senegal, Ghana, Côte d‘Ivoire, and Nigeria) have been moving toward achieving international standards in air safety oversight, but the remaining countries in ECOWAS are in need of significant development (figure 2.12). Additionally, only Nigeria and Ghana are equipped

|**Table 2.13 Evolution of market share**<br>|**of major reg**<br>|**ional carriers**<br>|**(%)**<br>|
|---|---|---|---|
|Airline|Share 2001|Share 2004|Share 2007|
|Air Senegal International|5.6|20.9|21.8|
|Virgin Nigeria (replaces Nigerian Airways)|||11.5|
|Bellview Airlines Ltd.|1.7|9.8|10.8|
|Société Nouvelle Air Ivoire||10.7|8.8|
|Air Burkina|2.6|9.4|7.5|
|Slok Air International|||7.4|
|Ethiopian Airlines Enterprise|3.9|3.4|7.0|
|Kenya Airways|0.1|0.2|5.1|
|SN Brussels Airlines|7.3||0.3|
|Air France|4.8||0.2|
|Air Afrique|32.0|||
|Ghana Airways Corp.|14.8|15.0||
|Cameroon Airlines|1.4|5.4||
|_Market concentration measures_||||
|Concentration top 3|54.1|41.4|44.1|
|Herfindahl index|13.8|10.0|9.9|

with air traffic control and weather information. Further regional cooperation could help to improve oversight and safety, through the pooling of scarce human resources and greater regulatory independence. About half of the ECOWAS member countries already belong to the Agence pour la Sécurité de la Navigation Aérienne en Afrique et à Madagascar (ASECNA), which, with support from France, provides some measure of air traffic safety. There is scope to broaden and deepen such collaborative arrangements.

The aging of the West African aircraft fleet is another aspect of the safety issue. In contrast to elsewhere in Africa, the West African fleet has aged significantly between 2001 and 2007. Across Africa, there has been substantial investment in new aircraft, leading to an overall renewal. This is not observed in West Africa, where the percentage of the fleet that is classified as ―western old‖ has increased from 36 to 43 percent between 2001 and 2007 (figure 2.13).

In the ECOWAS region, as well as throughout Africa, the need for new airports is not as great as the need for maintenance and additional capacity at existing facilities. In the case of Dakar, for example, calls for a new airport miss the point that a parallel taxiway could alleviate many of the runway constraints. In addition, a more efficient regional system could be envisioned if Lagos, for example, were turned into a regional hub, with turbo-prop flights into neighboring countries. Any such plans would most likely have to include substantial investment in terminal capacity.

40
**<u>Table 3: Returns to Education from Instrumental Variables: Russia, 2018</u>**

||Females|Males|
|---|---|---|
|Education, years|0.1430|0.0798|
||(-8.19)|(-3.43)|
|Experience|0.0313|0.0303|
||(-5.65)|(-4.3)|
|Experience squared|-0.0006|-0.0007|
||(-5.99)|(-5.61)|
|Urban|0.161|0.18|
||(-5.51)|(-5.69)|
|Constant|7.501|8.833|
||(-27.00)|(-26.65)|
|_N_|_2222_|_1694_|
|Centered_R_<sup>_2_</sup><br>|0.083|0.131|
|(i) Partial_R_<sup>_2_</sup>for excluded instruments in the first stage|0.105|0.093|
|F-test|43.63|34.43|
|_p-value_|_0.000_|_0.000_|
|(ii) Pagan–Hall for heteroskedasticity|5.78|9.973|
|_p-value_|_0.762_|_0.267_|
|(iii) Kleibergen-Paap rk LM statistic (underidentification test)|200.607|132.985|
|_p-value_|_0.000_|_0.000_|
|(iv) Sargan-Hansen J statistic (overidentification test)|10.395|20.158|
|_p-value_|_0.065_|_0.0005_|
|(v) Hausman endogeneity test|17.243|1.099|
|_p-value_|_0.000_|_0.295_|
|(vi) Cragg-Donald Wald F statistic|43.279|34.399|
|_Stock-Yogo critical values: 5% maximal IV relative bias_|_19.28_|_18.37_|
|_Stock-Yogo critical values: 10% maximal IV size_|_29.18_|_26.87_|

Note: z statistics in parentheses Source: RLMS To ascertain the statistical validity of the implemented instruments, we conducted an array of diagnostic tests using the Stata command _ivreg2_ . The lower panel of Table 3 shows the results from these tests. The F-test for possibility of weak instruments indicates that the instruments under focus are not weak; they are strongly correlated with the endogenous regressor. The Pagan-Hall tests indicate that errors are homoscedastic. The Kleibergen-Paap under-identification test further supports the null hypothesis, meaning that the instruments are relevant. The orthogonality of the set of instruments to the error process in the structural equation was checked by the Sargan-Hansen test of overidentifying restrictions; this is statistically significant for males, but for females the _p_ - value is 0.065. The Hausman endogeneity test shows that the education variable may not be endogenous for males therefore, there is no advantage to be gained from IV estimation for males, a finding already hinted at from the low difference between OLS and IV estimates for males. Finally, a Stock and Yogo's test points out that even if we are willing to tolerate (pp= 0.295); a 5 percent IV relative bias or 10 percent IV rejection rate at maximum, we can conclude that our instruments are not weak because the Cragg-Donald Wald F for both male and female sub-samples 16
- **Altonji, Joseph G and Lewis M Segal** , “Small-sample bias in GMM estimation of covariance structures,” _Journal of Business & Economic Statistics_ , 1996, _14_ (3), 353–366.

- **Ambrus, Attila, Eric Chaney, and Igor Salitskiy** , “Pirates of the Mediterranean: An empirical investigation of bargaining with asymmetric information,” _Quantitative Economics_ , 2018, _9_ (1), 217–246.

- **Ayres, Ian and Steven D. Levitt** , “Measuring Positive Externalities from Unobservable Victim Precaution: An Empirical Analysis of Lojack,” _The Quarterly Journal of Economics_ , 1998, _113_ (1), pp. 43–77.

- **Banerjee, Abhijit, Esther Duflo, Daniel Keniston, and Nina Singh** , “Crime, Punishment, and Monitoring: Deterring Drunken Driving in India,” Technical Report, Manuscript 2012.

- **Becker, Gary S.** , “Crime and Punishment: An Economic Approach,” _Journal of Political Economy_ , 1968, _76_ (2), 169–217.

- **Besley, Timothy, Thiemo Fetzer, and Hannes Mueller** , “The Welfare Cost of Lawlessness: Evidence from Somali Piracy,” _Journal of the European Economic Association_ , 2015, _13_ (2), 203–239.

- **Blundell, Richard, Luigi Pistaferri, and Ian Preston** , “Consumption inequality and partial insurance,” _The American Economic Review_ , 2008, pp. 1887–1921.

- **Clotfelter, Charles T.** , “Public Services, Private Substitutes, and the Demand for Protection against Crime,” _The American Economic Review_ , 1977, _67_ (5), 867–877.

- , “Private security and the public safety,” _Journal of Urban Economics_ , 1978, _5_ (3), 388 – 402.

- **Conley, Timothy G. and Christopher R. Udry** , “Learning about a New Technology: Pineapple in Ghana,” _American Economic Review_ , March 2010, _100_ (1), 35–69.

- **Corman, Hope and H. Naci Mocan** , “A Time-Series Analysis of Crime, Deterrence, and Drug Abuse in New York City,” _American Economic Review_ , 2000, _90_ (3), 584–604.

44
literature tries to investigate to what extent adverse shocks to a borrower's net worth increase the cost of extemal financing, and through which channels these adverse effects occur. For households, Mishkin (1977) provides evidence of effects of household balance sheet conditions on consumer expenditures in the US during the 1973-75 depression.

Kashvap and Stein (2000) study the monetarv-transmission mechanism using quarterly data on every insured US comm.ercial bank for the period 19764993. They find that the im.pact of monetary policy on lending is stronger for banks with less liquid balance sheets, i.e., banks with lower ratios of securities to assets. Moreover, this pattem is largely attributable to the smaller banks. Their results support the existence of a "bank lending channel" of monetary transmission, though they do not allow us to make precise statements about its quantitative importance.

Peek and Rosengren (2000) use the Japanese banking crisis as a natural experiment to test WCUhICI a **ivan.** suply shJLJck Lafe **CLI I** LA **.ea** ecor.or... act CLiI **v .t.** B uts **1I'J...I"lY** was" to **_V.J_** US credit markets, yet connected through the Japanese bank penetration of US markets, this event allows one to identify an exogenous loan supply shock and ultimately link that shock to construction activity in U.S. commercial real estate markets. They exploit the variation across geographically distinct commercial real estate markets to establish conclusively that loan supply shocks emanating from Janan had real effects on economic activity in the United States.

**tiAggrea teL 4aIU** LiULarciai shvcAs canar.%AL thevcopvratLe **ui1c** seoLrU **_by_ ULL** LailJing **ceitGAUL,** iUr.L1u1g working capital and trade financing, to borrowers with valuable trading and investment opportunities (see Kashyap and Stein (1994) for a review). Real, financial or regulatory shocks can cause a real or perceived shortage of capital for banks. As a result, banks may become unwilling to lend even to viable companies and instead may prefer to invest excess liquidity in safe assets such as government

4
# References

- Boer, G., G. Flato, and D. Ramsden (2000), “A transient climate change simulation with

- greenhouse gas and aerosol forcing: projected climate for the 21<sup>st</sup> century”, _Climate Dynamics_ **16** , 427-450.

- Dinar, A., R. Mendelsohn, R. Evenson, J. Parikh, A. Sanghi, K. Kumar, J. McKinsey, S. Lonergan (eds.), 1998, _Measuring the Impact of Climate Change on Indian Agriculture_ , World Bank Technical Paper No. 402, Washington, D.C.

- Emori, S. T. Nozawa, A. Abe-Ouchi, A. Namaguti, and M. Kimoto (1999), “Coupled Ocean-Atmospheric Model Experiments of Future Climate Change with an Explicit Representation of Sulfate Aerosol Scattering”, _J. Meteorological Society Japan_ **77** : 1299-1307.

- Houghton, J., Y. Ding, D. Griggs, M. Noguer, P. Van der Linden, X. Dai, K. Maskell, and

- C. Johnson (eds.) 2001, _Climate Change 2001: The Scientific Basil,_ Intergovernmental Panel on Climate Change, Cambridge University Press: Cambridge.

- Kelly, D.L., C.D. Kolstad, and G. T. Mitchell. 2005. “Adjustment Costs from Environmental Change.” _Journal of Environmental Economics and Management_ 50 (3): 468–95.

- McCarthy, J., O. Canziani, N. Leary, D. Dokken, and K. White (eds.) 2001, _Climate Change 2001: Impacts, Adaptation, and Vulnerability_ , Intergovernmental Panel on Climate Change Cambridge University Press: Cambridge.

Mendelsohn, R., W. Nordhaus and D. Shaw, "The Impact of Global Warming on 23
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE African Republic increased by about 10 percent from 1.31 to 1.43 million tonnes. The bulk of trade flows are from Cameroon to Chad (452,000 tonnes, of which 76,000 tonnes for gasoline and 64,000 for containers) and from the Central African Republic to Cameroon (252,000 tonnes, of which 237,000 tonnes of wood). Flows from Cameroon to the Central African Republic amount to 126,000 tonnes (of which 21,000 tonnes for gasoline and 20,000 for containers) while flows from Chad to Cameroon (57,000 tonnes), mostly involve cotton exports (49,000 tonnes) (World Bank 2011c).

# Roads

## **Achievements**

Cameroon’s road density is greater than that of peers. The density of the country’s total road network is 72 kilometers per 1,000 km2, higher than the average for Africa’s resource-rich countries at 59 kilometers per 1000 km2. But Cameroon’s road density is still behind the level of the continent’s middleincome countries, which have an average of 318 kilometers per 1,000 km2 (table 3).

Twenty-seven percent of Cameroon’s rural inhabitants have access to all-season roads, as measured using geographic information system (GIS) tools, slightly above the average GIS rural accessibility in resource-rich countries. But evidence from household surveys suggests that 51 percent of the rural population lives within 2 kilometers of an all-season road (table 3).

**Table 3. Cameroon’s road indicators benchmarked**

|Indicator|Unit|Resource-<br>rich<br>countries|Cameroon|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Road network density [a]|km/1000 km2 of land area|59|72|318|
|Classified road network density|km/1000 km2 of land area|38|51|278|
|GIS rural accessibility|% of rural pop within 2 km from all-season road|26|27|31|
|Household survey rural accessibility|% of rural pop within 2 km from all-season road|36|51|63|
|Classified paved road network condition [b]|% in good or fair condition|68|52|82|
|Classified unpaved road network condition|% in good or fair condition|61|65|58|
|Classified paved road traffic|Average annual daily traffic|1,402|1,099|2,558|
|Classified unpaved road traffic|Average annual daily traffic|25|60|75|
|Primary network overengineering|% of primary network paved with 300 AADT or less|20|16|18|
|Primary network underengineering|% of primary network unpaved with 300 AADT or more|9|36|20|

Source: AICD 2010b. _Note_ : a. Total network includes the primary, secondary, and tertiary networks. b. Classified roads are the roads that have been included in the roads legislation as public roads.

The existing fuel levy is set at an adequate level to cover road maintenance needs in Cameroon. At 9 cents per liter, Cameroon’s fuel levy is relatively high compared with other Sub-Saharan African peers (figure 7).

12
73

# Mozambique 1 PFP - Projectlons and Actuals

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# Mozambique 2 PFP - Projections and Actuals

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# Mozambiquo e PFP- Proloctions and Actuals

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