rafmacalaba/datause-encoder-data huggingface dataset:
less than 1 percent of urban infrastructure investment finance needs (US120 billion).

Over the last decade, this aid flow has at best been stagnant and possibly on the decline. The number of aid agencies providing significant funding is relatively small with only 10 donors accounting for 83 percent of all urban ODA. Kharas et al. see some signs of division of labor among principal donors, e.g., with the Asian Development Bank focusing on mega‐cities and city clusters, while the World Bank has focused on secondary cities. At the same time the number of urban projects has risen from about 200 in 1995 to almost 1,400 in 2007, while the average size of projects has dropped from US$1.9 million to US$1.3 million, with the median project cost in 2007 at only $90,000. This is about average for the various sectors of aid, but still reflects a remarkable degree of fragmentation into a proliferation of small projects.

In sum, urban engagement by donors has been relatively small and stagnant compared to other sectors and relative to financing needs, with relatively few donors, but with a highly fragmented set of interventions judging by the number of projects. Kharas et al. (2010) explore the reasons that might account for this apparent neglect of urban challenges by the donor community and identify four principal ones: (a) the now popular results orientation of donors, which is difficult to implement for urban programs, in view of the difficulty of measuring progress against difficult to quantify aggregate goals; (b) the focus of many donors on poverty reduction, which drives them towards a preference for rural programs; (c) donors’ stress on ownership, or alignment programs with local priorities, where in the past at least national governments have tended to neglect the urban agenda; and (d) donor agencies’ risk aversion in the face of difficult governance and social safeguard challenges in urban areas, which is particularly the case for projects requiring land acquisition and resettlement of people.

Urban advocates will have to find ways to overcome these obstacles to urban aid and research may be helpful in doing so, by focusing on the development of better urban aid data, development of clearer urban development results metrics, demonstration of the importance of urban poverty, articulation of a national‐level rationale for effective urban development and investigation of better means for urban land management.

2. <u>Design of partnerships in support of urban development</u> One area in which the urban sector appears to have an advantage over other areas of donor engagement is in its track record of development of innovative and effective coalitions of diverse partners. “Partnerships intuitively offer a way around some of the problems associated with today’s aid architecture, such as fragmentation and coordination. New urban partnership models have, at different times, combined the efforts of a range of different stakeholders: urban residents including slum dwellers, often arranged in community‐based organizations (CBOs) 20
to long-lasting institutional features.

Section I presents the analytical methodology and explains the econometrics. Section II documents the historical and geographical backgrounds of borders. Section III presents survey data and border discontinuities in development outcomes, first for the eastern border with Ghana and then for the northern borders. Section IV provides further discussion of the cash crop channel and national policies. Section V concludes.

# **I. Analytical methodology**

Here, we discuss the conditions under which the borders we study can be considered historical “natural experiments”. Consider a person born somewhere in the area now named Cˆote d’Ivoire. What would her welfare be if Cˆote d’Ivoire had been colonized by the British instead of the French, like Ghana, and then exposed to post-colonial Ghanian policies and institutions? This question is difficult to answer for at least three reasons: (i) the British could have established different institutions to rule Cˆote d’Ivoire; (ii) Ghana and Cˆote d’Ivoire combined would not be the same, if only because of market size and general equilibrium considerations; and (iii) even if Ghana’s institutions had remained the same, we have no idea how Cˆote d’Ivoire’s initial characteristics would interact with them. Now, take a person born in Cˆote d’Ivoire at the border of Ghana and imagine that the border was some kilometers farther. Including this person in Ghana would have a marginal and insignificant impact on Ghana’s institutions and economy. Furthermore, it is very probable that this close neighbor of actual Ghanaian people would share the same geographical constraints and the same precolonial initial conditions.

6
as precipitation. Therefore, it can provide more accuracy in socio-political studies investigating conflict and cooperation over water.

In particular, results from this paper demonstrate that the BWI and rainfall runoff models did have a highly significant explanatory power of down-stream gauge measurements; however the BWI model has higher accuracy and significance level compared to rainfall in the Zambezi, while a negligible difference exists in the Mekong. Moreover, the accuracy of the BWI increased under low flow conditions, which reveals its utility in high risk situations, allowing it to serve as an independent measure of risk and its probability of occurrence. Results from the modeled flow in the Zambezi illustrate its potential value as a predictive tool for many river basins around the world. Specifically, the BWI-based model accurately predicted from a satellite the magnitude of floods months in advance. It accomplished this result by measuring the magnitude of water entering the watershed upstream of the lower basin.

Building on the results from this study, future work will refine the methodology and applications. Specifically, the methodology can be extended beyond the stringent set of assumptions in this paper thereby allowing policy makers to monitor flow in regulated rivers. In addition, the basin economic model can benefit from further development and expansion of the model to other river basins.

# **REFERENCES**

Adger, Neil, Terry P. Hughes, Carl Folke, Stephen R. Carpenter, and Johan Rockström. 2005.

- ―Social ecological resilience to coastal disasters,‖ _Science_ , 309(5737): 1036-1039.

- AFROL.COM. 2009. Online resource, accessed 2009. www.afrol.com/articles

- Alcamo, J., P. Döll, T. Henrichs, F. Kaspar, B. Lehner, T. Rösch, and S. Siebert. 2003. ―Global estimates of water withdrawals and availability under current and future business-asusual conditions,‖ _Hydrological Sciences Journal_ , 48: 339-348.

- Alsdorf, D, E Rodriguez, and D P Lettenmaier. 2007. ―Measuring Surface Water from Space,‖ _American Geophysical Union_ , 45(2): 1-24.

- Anderson P and Meerschaert M. 1998. ―Modeling river flows with heavy tails,‖ _Water Resources Research_ , 34: 2271–80.

23
**TABLE 2 EGYPT 1975-80**

**DECOMPOSITION OF THE TOTAL IMPACT OF WEANING ON CHILD MORTALITY (before age 60 months)**

WEANED ceased breastfeeding prior to child death PREGNANT mother became pregnant after weaning and prior to child death RR relative risk of child death (weaned / not weaned)

-FERTILITY CONTROL-------ALL ln(RR) % NO ln(RR) & YES ln(RR) % WEANED RR at anyage TOTAL 2.43 100.00 i 2.93 100.00 **'1.09** 100.00 % DIRECT 2.06 81.45 % 2.21 73.75 % * INDIRECT 1.18 18.55 % 1.33 26.25 % 1.09 100.00 % (through pregnant) WEANED RR before 12 months TOTAL 5.29 100.00 % 6.94 100.00 % 3.87 100.00 % DIRECT 4.29 87.44 % 5.13 84.39 % 3.35 89.37 % INDIRECT 1.23 12.56 % 1.35 15.61 % 1.15 10.63 % (through pregnant) WEANED RR at 12 months or later TOTAL 1.95 100.00 % 2.60 100.00 % * DIRECT 1.95 100.00 % 2.60 100.00 % * INDIRECT * * * (through pregnant)

based on Weibull proportional hazards and log-logistic models controlling for: next pregnancy of mother, weaning, parity, age of mother, preceding birth interval length, sex, region, urban/rural residence, fertility control, education of mother, education of father indirect effects evaluated at mean age of pregnancy based on data from the Egyptian Fertility Survey 1980 restrictions: children born within 60 months of survey to married women last two children only no multiple births no marital disruption prior to child death Muslim only

*-not statistically distinguishbble from 1.0

34
Table 9. Minutes spent within the day across key activity categories, by treatment arm

||T|otal|W|omen||Men|
|---|---|---|---|---|---|---|
||Recall|Smartphone|Recall|Smartphone|Recall|Smartphone|
|Morning (12am-11:59am)|||||||
|Agriculture|59.6***|35.2***|53.1***|35.3***|66.2***|35.1***|
|Non-agriculture|54.9***|18.1***|28.4***|11.1***|81.7***|24.9***|
|Leisure|158.3***|75.4***|135.2***|61.7***|181.7***|89.0***|
|Unpaid work|109.2***|82.6***|177.4***|134.4***|40.0***|31.4***|
|Transport|62.8|65.7|55.2|52.7|70.4**|78.5**|
|Eating/drinking|8.4***|6.8***|8.3***|6.6***|8.4**|7.0**|
|Afternoon/evening/night (12pm-11:59pm)|||||||
|Agriculture|18.9|17.9|15.7|16.1|22.3|19.7|
|Non-agriculture|58.2***|70.1***|29.5**|41.1**|87.2|98.7|
|Leisure|306.2**|294.1**|267.8|256.6|345.2|331.1|
|Unpaid work|103.7***|133.3***|176.6***|210.3***|29.8***|57.1***|
|Transport|64.7***|95.1***|48.7***|64.5***|81.0***|125.3***|
|Eating/drinking|44.4***|39.9***|42.7|40.6|46.1***|39.2***|
|Evening/night (6pm-11:59pm)|||||||
|Agriculture|1.1***|3.2***|0.7***|2.2***|1.5**|4.1**|
|Non-agriculture|14.3***|25.1***|6.0***|16.9***|22.7**|33.3**|
|Leisure|106.8***|133.5***|86.5***|106.3***|127.4***|160.5***|
|Unpaid work|31.9***|49.6***|56.0***|82.6***|7.5***|17.0***|
|Transport|13.7***|34.5***|7.0***|20.5***|20.6***|48.4***|
|Eating/drinking|21.7|21.1|20.7|21.4|22.7*|20.8*|

Notes: (1) T-tests of equality of means conducted across treatment groups; ***p<0.01, **p<0.05, *p<0.10.

(2) Based on a pooled individual-level sample for days 5, 8 and 10 (each individual appeared three times in the sample).

(3) Grey shading = recall arm yields significantly lower estimates.

One possibility for these differences is respondent fatigue in the recall arm, given the structure and relatively long duration of the interview. Respondents might be more actively reporting time in different productive activities at the start of the interview, and by the end of the narrative (i.e., for hours later in the day) they lose interest or track of their time. Figure 6 gives some indication that this may be the case, revealing a bimodal sleep distribution for recall respondents that is particularly pronounced for women. A significantly greater share of recall respondents, particularly women, report earlier sleep times that would conclude the diary interview — 64 percent of women in the recall arm reported sleep times between 5 pm8:59 pm, versus 54 percent in the smartphone arm. For men, these shares were 50 and 45 percent, respectively. This supports, to some extent, that fatigue may be a key factor. Other factors may be at play as well, however, including potential difficulties among those reporting earlier sleep times to pinpoint in the interview exactly when they went to sleep.<sup>20</sup>

> 20 Also see Kaplan et al., 2020, for a discussion on recalling sleep times using the American Time Use Survey.

22
|AR-GEF Sustainable and Transport and AirQualityProject|Veronica Ines Raffo|
|---|---|
|AR-GEF Sustainable and Transport and AirQualityProject|Veronica Ines Raffo|
|AR-GEF Sustainable and Transport and AirQualityProject|Veronica Ines Raffo|
|AR-GEF Sustainable and Transport and AirQualityProject|Veronica Ines Raffo|
|AR-GEF Sustainable and Transport and AirQualityProject|Veronica Ines Raffo|
|AR-GEF Sustainable and Transport and AirQualityProject|Veronica Ines Raffo|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|BR-GEF Sustainable Transport and AirQualityProject(STAQ)|Georges Bianco Darido|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Sustainable Transport and AirQuality|Felipe Targa Rodriguez|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|Urban Youth Employment Project|Sonya Woo|
|ShandongEnergyEfficiencyProject|Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject|Jonathan Edwards Sinton|
|ShandongEnergyEfficiencyProject|Jonathan Edwards Sinton|
Nagypal, E. (2004). “Reallocation over the Business Cycle: The Importance of Job-toJob Transitions” Draft, Northwestern University, July 2004.

Perry, G., W. Maloney, O. Arias, P. Fajnyzlber, A. Mason and J. Saavedra, _Informality: Exit and Exclusion_ . Washington, D.C., World Bank, ©2007.

Petrongolo, B. and Pissarides, C. (2008) "The Ins and Outs of European Unemployment". _American Economic Review, P&P_ , May 2008.

Pissarides, C. A.(2000). _Equilibrium Unemployment Theory._ Cambridge: MIT Press.

Pissarides, C. A. (2007). “The Unemployment Volatility Puzzle: Is Wage Stickiness the Answer?,” The Walras-Bowley lecture, North American Summer Meetings of the Econometric Society, Duke University, June 21-24, 2007 Schneider, F. and Enste, D. (2000). "Shadow Economies: Size Causes, and Consequences," _Journal of Economic Literature_ , 38(10): 77-114.

Shimer, R. (2007), “Reassessing the Ins and Outs of Unemployment” Mimeo, University of Chicago.

Shimer, R. (2005) “The Cyclicality of Hires, Separations, and Job to Job Transitions,” _Federal Reserve of St. Louis Review,_ July/August. 87 (4), pp. 493-507.

Rauch, J. E., 1991. "Modeling the Informal Sector Formally," _Journal of Development Economic_ s, Elsevier, vol. 35(1), pages 33-47, Rogerson, R., Shimer, R. and Wright, R. (2005), “Search Theoretic Models of the Labor Market” _Journal of Economic Literature_ , 43 (4): 959-988.

Zenou, Y. “Job Search and Mobility in Developing Countries. Theory and Policy Implications,” Forthcoming _Journal of Development Economics_ .

29
interpretation that we are in fact estimating the production function instead of, for instance, an infrastructure demand equation, or a combination of both relations.

Our estimates of the output contribution of infrastructure are significant not only statistically, but also economically. To illustrate this, consider an increase in the level of infrastructure provision from the cross-country median in the year 2000 (an index of -4.65, roughly similar to the value observed in Tunisia in 2000) to the 75<sup>th</sup> sample percentile in the same year (an index of -3.69). This would translate in a 7.7 percent (=0.08*(-3.69+4.65)) increase in output per worker.<sup>30</sup> Similar calculations show that: (a) an increase in infrastructure provision from the median level observed among lower-middle income countries (-4.67, roughly equivalent to Bolivia in the year 2000) to that of the median upper-middle income country (- 4.02, Uruguay) would yield an increase in output per worker of 5.2 percent, and (b) raising the level of infrastructure provision from the value observed in the median upper-middle income country to that of the median high-income country (-2.93, corresponding to Ireland) would raise output per worker by 8.7 percent.

# **5. Conclusions**

This paper adds to the empirical literature on the contribution of infrastructure to aggregate output. Using an infrastructure-augmented production function approach, the paper estimates the output elasticity of infrastructure on a large cross-country panel dataset comprising over 3,500 annual observations. The paper addresses several limitations of earlier literature. It uses a multi-dimensional concept of infrastructure, combining power, transport and telecommunications infrastructure into a synthetic index constructed through a principal component procedure. The econometric approach deals explicitly with the non-stationarity of infrastructure and other productive inputs, reverse causality from output to infrastructure, and potential cross-country heterogeneity in the contribution of infrastructure (or any other input) to aggregate output.

The empirical strategy involves the estimation of a production function relating output per worker to non-infrastructure physical capital, human capital, and infrastructure inputs. Our estimates, based on heterogeneous panel time-series techniques, place the output elasticity of infrastructure in a range between 0.07 and 0.10, depending on the precise specification

> 30 Note that 0.08 is the estimated coefficient of the infrastructure index in regression [1] of Table 3.

21
significantly over time.<sup>11</sup> This reflects the fact that although more women are migrating over time, more men are also doing so at an increasing rate. Women under 45 work in agriculture at about the same rate as men before 2006, echoing the findings of de Brauw et al. (2008). However, a gap emerges in the 2006 data with women from their 30s more likely to be employed in agriculture than men in the same age groups (Figure 1b).<sup>12</sup> Women at older ages continue to be less likely to work in agriculture. Women’s participation in local non-farm work (whether wage or off-farm family businesses) has also lagged behind men but shows signs of catching up over time, especially at younger ages.

We conclude from the above descriptive information that, with the exception of the very youngest, women are indeed lagging behind in leaving farms and seeking migrant employment opportunities. This gender gap is not closing but widening over time. Parallel to this trend, a higher percentage of women are staying behind in rural areas to run the farms and engage in local off-farm employment.

# **_4.2 Women’s migration_**

Although women in most age groups are being left behind, some still migrate. What factors affect whether a rural woman migrates for work? The panel and the repeated observations over time on rural individuals allow us to identify non-migrant women ─ here defined as women aged 7 to 52 in 1997 (16 to 61 in 2006) who report no migration experience during the CHNS panel survey period ─ and women in the same age group who do. Among the 2592 observations with non-missing values for key variables, 563 (22%) ever migrated in the subsequent years. Using probit models and graphical techniques, we explored what initial 1997 individual, household and geographic/village characteristics are associated with a higher probability of subsequent migration. Since the specific probit values are not the main concern of the paper, we limit discussion to a brief summary of the results here and illustrate some of the more striking effects in Figure 2. (The probit is given in Appendix Table 2.)

> 11 These are non-parametric regressions of locally weighted smoothed scatter plots.

> 12  An OLS regression suggests that, conditional on province fixed effects, women in this group are 8% more likely than men to work in agriculture. This gender difference is statistically significant at the 5% level.  The increase in the gender difference is also statistically significant at the 5% level compared to 1997 when women in the same age group are only 1.5% more likely to work in agriculture.

11
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18