rafmacalaba/datause-encoder-data huggingface dataset:
(MDBs) may systematically prefer certain contractual forms or projects with larger required investments.

In this paper, the probability of receiving MLS at the project level is estimated to serve as a metric –a _means_ by which to compare PPP projects with and without MLS. To this end, project-level and aggregated country- and regional-level factors are taken into consideration to identify most-similar sets of PPPs with and without MLS for later comparison with respect to cancellation rates.

# **_Data_**

The statistics and probabilistic models in this study follow the definition of PPP adopted by the PPI Database. According this source, infrastructure projects with private participation (PPI) that have reached financial closure excluding divestitures, telecom projects, or merchant<sup>4</sup> projects are considered PPPs. Using this definition, of the full PPI data set of 7,042 projects, 5,054 are PPP projects. Of these, 637 involve MLS and 4,417 do not (Figure 1). This PPP project sample is used to estimate the likelihood of receiving multilateral support.

Figure 1. Project data set for econometric models: PPPs from 1990-2015 <!-- Start of picture text --> PPIs<br>(7,042 projects)<br>PPPs<br>5,054 projects (71%)<br>With MLS Without MLS<br>637 PPPs (12.6%) 4,417 PPPs (87.4%)<br><!-- End of picture text --> Source: PPI Database, Feb 2016 The project outcome analyzed in this paper –cancellation rate– is based only on PPP projects in that are currently classified as operational, concluded, or canceled; and have reached financial closure between 1990 and 2010 (i.e., PPPs that have survived five or more years). Of the 5,054 PPPs in the PPI Database, 3,552 projects have survived more than 5 years, of which 2,777 are operational or concluded, whereas 173 are canceled (Table 1).

The main reason not to consider relatively young PPPs (less than six years of potential operability) is simple: a project’s outcome (e.g., conclusion, cancellation, survival) can only be reasonably measured when projects have been operational for enough time to allow their outcomes to become observable.<sup>5</sup> For example, PPP

> 4. Merchant projects are those wherein “a private sponsor builds a new facility in a liberalized market in which the government provides no revenue guarantees. The private developer assumes construction, operating, and market risk for the project.”

> 5. According to the PPI Database, canceled projects have an average duration (between the financial closure year and its cancellation) of 5.89 years. Moreover, only one of the PPP projects that reached financial closure after 2012 has been canceled.

4
**_<u>________________________________________________________</u>_**

# **Annex C.   Infrastructure Indicators**

|||**Latin A**|**merica**|||**Asia**|||
|---|---|---|---|---|---|---|---|---|
||**Brazil**|**Honduras **|**Nicaragua**|**Peru**|**Bangladesh **|**China**|**India**|**Pakistan**|
|% of production/sales<br>lost due to power<br>outages|2.5|5.5|6.2|3.3|3.3|2.0|8.9|5.4|
|Have own generator<br>(%)|16.8|31.6|21.6|27.6|71.5|27.2|60.8|41.8|
|No. of days to obtain<br>a telephone<br>connection|18.2|136.0|117.0|9.9|154.8|15.6|46.8|29.5|
|No. of days to obtain<br>an electricity<br>connection|25.5|29.8|23.8|22.5|79.6|29.0|85.0|38.2|
|Days to clear customs<br>(for imports) - average|14.0|6.2|5.1|7.8|11.7|7.9|7.1|17.2|
|Days to clear customs<br>(for imports) - longest|32.0|13.8|10.6|15.7|23.2|12.5|12.8|30.1|
|Days to clear customs<br>(for exports) - average|8.7|2.1|1.8|5.2|8.8|5.4|5.4|9.6|
|Days to clear customs<br>(for exports) - longest|16.8|3.4|3.0|--|14.0|8.0|8.0|17.1|
|Share of foreign<br>invested firms|5.1|9.3|12.4|8.3|3.2|25.4|2.5|1.9|
|Share of firms that<br>export|30.7|27.9|24.6|28.8|42.4|29.7|21.9|19.8|

_Source : Dollar, Hallward-Driemeier and Mengistae (2003)_ 49 ____________________________________________________________________________________________________________
cash transfers participated over the course of the program. In addition to cash transfers, a subset of beneficiary villages was randomly selected to receive a behavioral change component to encourage investments in children’s human capital. The value-added of this behavioral intervention on early childhood development outcomes is the focus of the original RCT discussed in Premand and Barry (2020). In the present paper, we focus on the pooled cash transfer treatment and its effect on household-level outcomes related to resilience.<sup>15</sup> A follow-up household survey was implemented between mid-January and mid-April 2015. From the original sample, 3,953 households were included in the sample from the follow-up survey, of which 3,803 were tracked at follow-up with complete questionnaires.<sup>16</sup> The attrition rate (3.8 percent) is balanced between treatment and control. The follow-up survey included a household survey questionnaire similar to the baseline survey, with the addition of a few modules on agriculture and other activities.

To complement the survey data, we compiled meteorological satellite data to obtain exogenous measures of climate shocks. Specifically, the follow-up survey collected geo-coordinates for each household, and we calculated the median geo-location for each village. For this analysis, we separate hamlets ( _hameau_ in French) from villages, because hamlets are related to a village administratively but can be geographically relatively distant. In doing so, we obtain 512 villagehamlet units with unique GPS coordinates. We then merged the household data with satellite data that contained information on climatic variables based on the GPS coordinates of their village-hamlet. The satellite data were obtained from the African Flood and Drought Monitor (AFDM)<sup>17</sup> and include daily rainfall information for the period 1970-2015 at a 0.25 degree resolution. From this information, we constructed the monthly historical average level of rainfall for each of the 512 village-hamlet units for the period that precedes the intervention (1970-

> 15 The analysis of the impact of cash transfers on resilience was not planned when the RCT was designed. However, this question became central both as a research and policy issue during implementation of the Niger safety net project, in particular when the Sahel Adaptive Social Protection Program was set up and provided additional financing to the project with a core objective to improve household resilience.

> 16 The follow-up sample was stratified based on the proxy means test score “predicted” from the baseline survey. We dropped from the sample 377 households with high predicted PMT score (hence low probability of being selected as beneficiary) (see Premand and Barry, 2020).

> 17 <u>https://platform.princetonclimate.com/PCA_Platform/.</u>

11
30 magnitude of the mortality coefficient is related to it's modest size relative to the fertility coefficient. However the relative magnitude of the estimated interaction coefficients is consistent with the literature. A specific finding of the sensitivity analysis is, thus, that the size and significance of the mortality interaction coefficient is critical to the estimates of cost effectiveness and should be the subject of refinement.

36. In spite of the sensitivity of the results to the estimated mortality interaction coefficient, the range of cist effectiveness estimates generated by the sensitivity tests confirms the importance of mortality interaction. Comparing the estimates of cost effectiveness in table 4, based on the estimated coefficients, with the range of estimates in Table 5, based on the sensitivity analysis, it is seen that the estimates of cost per unit of

# **Table S**

SENSITIVITY OF COST EFFlETrrVZ0 SS OF FAMILY PLANNING AND NON HOSPITAL HEALTH PROGRAMS TO CHANGES IN THE INTERACTION COEFFICIENTSI/ (US$)

|||COST PER||
|---|---|---|---|
||Birth<br>Prevented|Infant Death<br>Averted|Adjusted Infant<br>Death Avertedh/|
|Total EffectsS/||||
|Family Planning Progr.-m<br>Variation of:||||
|TFR Coef. in IMR Eq.|75-82|383-540|863-1890|
|IMR Coef. in TFR Eq.|62-83|359-483|959-1287|
|Non Hosprtal Health Programs<br>Variation of:||||
|TFR Coef. in IMR Eq.|1135-1243|<br>1752-1918|<br>2102-2302|
|IMR Coef. in TFR Eq.|378-2540|<br>1168-2138|<br>-<br>1752-2352|

£/ The table gives the range of cost effectiveness of family planning and non hospital health programs as the interaction coefficient on IMR varies from .1 to .5 and the interaction coefficent on TFR varies from .4 to .8.

See Table 4 for an explanation of munadjusted" infant deaths averted.

£/ Direct effects are not affected by changes in the interaction coefficients.
# **1 Introduction**

Sub-Saharan Africa has experienced substantial economic growth in the past decade, after a quarter century of stagnation and contraction.<sup>1</sup> It is perhaps too early to answer the question of why this period of growth has arrived. However, it is important to know where it has happened within countries and why these places have been favored.

In this paper, I argue that changes in transport costs have played a critical role in determining the growth of cities. Sub-Saharan Africa has notoriously high transport costs compared to other major regions of the world. Population density is relatively low, with a substantial fraction of people residing far from the coast. Ocean-navigable rivers, which provide transport to the interior of most other regions, are virtually non-existent. And road networks are sparse and poorly maintained, on the whole. I ask whether periphery cities with lower transport costs to their country’s main port grew faster than those farther away or with poorer road connections, in the context of dramatically rising oil prices over the 2000s decade. A typical problem with testing this kind of question in poor countries is that relevant data on cities and transport costs do not exist. This paper provides novel measures of both. First, night time lights satellite data (Elvidge et al., 1997; Henderson, Storeygard and Weil, 2012) are used to construct a 17-year annual panel of city-level measures of economic activity for 287 cities in 15 countries. Second, a new set of roads data provides information about route length and surface material. Transport costs are thus identified by the interaction between world oil prices and distance along these routes. Because I have data on many cities per country over a substantial time period, I can control for annual shocks separately for each country, as well as initial characteristics and growth rates of individual cities.

Focusing on countries whose largest, or _primate_ , city is also a port, I find that as the price of oil increases from $25 to $97 (as it did between 2002 and 2008), if city A is 465 kilometers (1 standard deviation) farther away from the primate than initially identical city B, its economy is roughly 6 percent smaller than city B’s at the end of the period. At a differential of 2,360 kilometers, the largest in the data, this rises to 32 percent. Further evidence shows that this effect is due to transport costs, not commodity income or the generation of electricity. I then determine that this effect falls disproportionately on cities that are connected to the primate by paved roads, most likely because they are initially more engaged in trade. Cities connected to the primate by unpaved roads appear to be more affected by transport costs to secondary cities. This suggests a

> 1This paper generally defines Sub-Saharan Africa as all countries of the African mainland with no Mediterranean coastline, plus Madagascar.
and Sleuwaegen, L. and Goedhuys, M. (2002), respectively analyzing 141 and 129 manufacturing firms in Côte d’Ivoire, also found negative correlations between firm growth and both firm size and age; finally, running GMM-SYS panel estimates covering census-based Ethiopian manufacturing firms over the period 1996-2003, Bigsten and Gebreeyesus (2007) showed how the negative relationship between size and age on the one hand and firms’ employment growth on the other is significant and robust to sample selection and unobserved firm heterogeneity<sup>21</sup> .

# **<u>4.2.2 Credit rationing</u>**

Credit constraints and lack of financial capital in general should limit the rate of entry of new businesses, and their likelihood of survival and rate of growth (see Xu, 1998; Carpenter and Petersen, 2002; Becchetti and Trovato, 2002; Aghion, Fally and Scarpetta, 2007). However, some recent studies have shown that the role of credit rationing has been somewhat over-emphasized and that entrepreneurial saving plans may be able to overcome borrowing constraints (Cressy, 1996 and 2000; Parker, 2000; Hurst and Lusardi, 2004). The risk of overstating the hindering role of credit constraints is particularly high in questionnaire analyses where nascent or newborn entrepreneurs are asked to list their main difficulties in starting and/or running a new firm; in fact, they have the self-indulgent tendency to indicate a lack of external financial support as the main _cause_ of their problems, while in most cases this is just a _symptom_ of more fundamental deficiencies internal to the firm.

At any rate, as already discussed in Section 4.1, new entrepreneurial initiatives in the DCs are credit-rationed in the vast majority of cases due to lack of collateral, informational asymmetries and largely imperfect local capital markets. For instance, Goedhuys and Sleuwaegen (2010), in a study investigating 947 small and medium entrepreneurial firms in several manufacturing firms in eleven Sub-Saharan African countries<sup>22</sup> , report that financial constraints are singled out as the major obstacle (from between eleven

> 21 Consistent econometric outcomes can also be found in Mead and Liedholm (1998); Gunning and Mengistae (2001); Bigsten and Söderbom (2006); Coad and Tamvada, 2012. 22 The authors extracted their firm-level data from the World Bank Investment Climate Survey.

20
**Figure 4. Percentage of firms that reported the following issues as obstacles to their activity India**

<!-- Start of picture text --> 36% 35% 38%<br>29% 28% 27%27% 28%<br>13% 15%14% 14%17% 15%13% 14% 15%19% 17%21% 21% 17% 15% 17%<br>4% 5% 7% 10%10% 9% 10% 8% 9% 8% 5%<br>0%<br>Telecom Electricity Transport Access to Tax rates Tax adm. Trade Reg. Labor Reg. Workers Licensing Access to Cost of Fin Ec. Policy Macro Corruption Crime Anti- Legal<br>Land Skills Fin Instability competition System<br>2005 2002<br><!-- End of picture text --> 29
19 moderately. Equidispersion seems to be less of an issue in the short-term, where the varianceto-mean-ratio is about 1.1, but slightly more so in the long-term, where the variance-to-meanratio is about 1.7. A formal test establishes that over-dispersion is present in most estimations, indicating that the variance is greater than the mean, which would underestimate standard errors. We correct for this by computing robust (and cluster-specific) standard errors. This still retains consistent estimates if the conditional mean function is correctly specified (Cameron and Trivedi 2009: 561). The fitted probabilities of the Poisson model are acceptable, with the maximum difference in predicted and actual probabilities of the count variable ranging between 0.08 (short term) and 0.1 (long term). The Poisson prediction of zero counts is relatively precise, while the prediction of two children is the least precise. Other regression diagnostics (chi-squared value, log-likelihood statistics, AIC, BIC, and the squared coefficient of correlation between fitted and observed values of the dependent variable) confirm that Poisson estimations fit the data reasonably well. Overall, the model fit in the middle age group, which does not exhibit any natural censoring in its fertility, is much better than the fit for the young and old age groups.

# **a) Conflict and fertility in the short term**

Women surveyed in 2000 who lost at least one child during the genocide (CHILDDEATH) gave birth to a significantly larger number of children in the short term than other women (Table 5). This findings supports the replacement hypothesis: The estimated fertility rate of these women is factor exp(0.14) = 1.15 times the predicted fertility rate of women not affected by conflict, controlling for the number of sons and daughters previously born and socio-economic factors. Moreover, the gender of the lost child matters: The death of a son (SONDEATH) results in significantly higher fertility in the post-war period, while the loss of a daughter (DAUGHTERDEATH) does not affect fertility. This result is in line with expectations, given that Rwanda is a patrilineal society. Women used to get access to land and resources primarily through men, and sons traditionally set up their own household close to their parental homestead (Hamilton 2000).<sup>10</sup> Interestingly, we find the opposite effect for sibling death. Women in 2000 who lost a sibling (SIBLINGDEATH) during the genocide have significantly lower fertility (with a factor effect

> 10 New legislation on succession and marital property regimes became law in 1999, granting women the right to hold ownership of property, including land, and to inherit (Burnet and RISD 2001).
86 Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi (2003). “Governance Matters III: Governance Indicators for 1996-2002.” _Policy Research Working Paper Series_ . No. 3106. The World Bank. July.

Kornai, Janos (1992). _The Socialist System: The Political Economy of Communism._ Princeton, N.J.: Princeton University Press.

Knack, Stephen and Philip Keefer (1995). “Institutions and Economic Performance: Cross Country Tests Using Alternative Institutional Measures _.” Economics and Politics_ . Vol. 7, No. 3, pp. 207-227.

Krueger, Allan B. and Mikael Lindahl (2000). “Education for Growth: Why and For Whom?” National Bureau of Economic Research Working Paper No. 7591.

Lederman, Daniel and William F. Maloney (2003). “R&D and Development.” _Policy Research Working Paper_ No. 3024, The World Bank.

Levine, Ross E. and David Renelt (1992). “A Sensitivity Analysis of CrossCountry Growth Regressions.” _American Economic Review_ . Vol. 82, No. 4, pp. 942963.

Levine, Ross, Norman Loayza and Thorsten Beck. “Financial Intermediation and Growth: Causality and Causes _._ ” _Journal of Monetary Economics_ . Vol. 46, 2000, pp. 3177.

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to which the variation during the Farm Bill and ARRA are closely chained. We find that results are similar using the share of R-squared explained by each variable as an alternative following Huettner and Sunder (2012).<sup>19</sup> Anticipating the discussion of controls, we assess how the results change with further inclusion of housing prices, unemployment rates, average state wage bills, average state income, as well as average state income and rent of program participants as controls. We find that, among these, the housing-price control is sufficient for meaningful precision, because the program scale also varied over this period, and housing prices and retail prices were strongly positively correlated during this period. Including the housing-price control substantially improves the first stage.<sup>20</sup> Results are robust to the inclusion or exclusion of any other combination of controls.

We show in Appendix Table N1 that our conclusions about the sources of variation in the IV remain unchanged even after controlling for this extensive list of controls, which together explain a negligible proportion of the variation in the IV. The vector of state-level changes in energy usage and prices does explain about 10%-20% of the IV variation, but the SUA continues to drive variation in the IV, suggesting that idiosyncratic prediction errors are accounting for much of the residual variation. As mentioned, our results are robust to controlling for these energy usage and price variables.

# **2.3 Price Indices**

## **2.3.1 Nielsen Retail Scanner**

We use the Nielsen Retail Scanner Dataset through a partnership between the Nielsen Company and the James M. Kilts Center for Marketing at the University of Chicago Booth School of Business.<sup>21</sup> The data consists of weekly pricing, volume, and store merchandising conditions generated by participating retail store point-of-sale systems across the US from 2006-2015.<sup>22</sup> Data is included from approximately 35,000 participating stores and include store types such as drug, grocery, and mass merchandise stores, covering around 53%-55% of

> 19This measure uses Shapley and Owen values, which calculate the average marginal contribution of each regressor over all possible orderings.

> 20As discussed in Section 4.1, this occurs given that the benefits series contains a large disturbance term, muting the observable first stage; the housing-price control helps to net out this disturbance term, improving relevance.

> 21Information on access to the retail scanner data as well as the consumer panel data below is available at `http://research.chicagobooth.edu/nielsen/` .

> 22The data measures the weighted average weekly price of each good at the store-level, which includes retailer discounts such as loyalty cards and coupons. We find minimal economically meaningful changes to shopping behavior of households, suggesting that discounts and coupons do not explain our findings of price incidence (see Appendix Table N13).

12
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS 842 Capital Flows to South Asia and<br>ASEAN Countries: Trends,<br>Determinants, and Policy Implicatio|lshrat Husain<br>Kwang W. Jun<br>ns|January 1992|S. King-Watson<br>31047|
|WPS843 How Financial Markets Affect Long-<br>Run Growth: A Cross-Country Study|Ejaz Ghani<br>|January 1992|A. Nokhostin<br>34150|
|WPS844 Heterogeneity, Distribution, and<br>Cooperation in Common Property<br>Resource Management|Ravi Kanbur|January 1992|WDR Office<br>31393|
|WPS845 Inflation Stabilization in Turkey:<br>An Application of the RMSM-X Model|Luc Everaert<br>|January 1992|B. Mondestin<br>36071|
|WPS846 Incorporating Cost and Cost-<br>Effectiveness Analysis into the<br>Development of Safe Motherhood<br>Programs|Larry Forgy<br>Diana M. Measham<br>Anne G. Tinker|January 1992|0. Nadora<br>31091|
|WPS847 Coping with the Legacies of<br>Subsidized Mortgage Credit in<br>Hungary|Silvia B. Sagari<br>Loic Chiquier|January 1992|M. Guirbo<br>35015|
|WPS848 How EC 1992 and Reforms of the<br>Common Agricultural Policy Would<br>Affect Developing Countries' Grain Tr|Meriinda D. lngco<br>Donald 0. Mitchell<br>ade|February 1992|P. Kokila<br>33716|
|WPS849 Financial Structures and Economic<br>Development|Ross Levine|February 1992|W. Pitayatonakarn<br>37666|
|WPS850 Fiscal Adjustment and the Real<br>Exchange Rate: The Case of Bangla|Kazi M. Matin<br>desh|February 1992|D. 3allantyne<br>38004|
|WPS851 Sources of World Bank Estimates<br>of Current Mortality Rates|Eduard Bos<br>My T. Vu<br>Patience W. Stephens|February 1992|0. Nadora<br>31091|
|WPS852 How Health Insurance Affects the<br>Delivery of Health Care in Developing<br>Countries|Joseph Kutzin<br>Howard Barnum|Fsbruary 1992|0. Nadora<br>31091|
|WPS853 Policy Uncertainty, Information<br>Asymmetries, and Financial<br>Intermediation|Gerdrd Caprio|February 1992|W. Pitayatonakarn<br>37664|