rafmacalaba/datause-encoder-data huggingface dataset:
PROVISION OF WATER TO THE POOR IN AFRICA

**Table 1.2  Urban households connected and unconnected to piped water by region**

Percent

|||||Unconnected<br>|<br>|||
|---|---|---|---|---|---|---|---|
|Region|Connected|All unconnected|Standpipes|Wells and boreholes|Surface|Vendor|Other|
|West|28|72|21|37|6|7|0|
|Central|34|66|32|14|16|1|3|
|East|37|63|31|20|8|1|3|
|South|65|35|22|9|3|0|0|
|Total|38|62|25|24|7|4|2|

Source: AICD DHS/MICS Database, 2007 Most utilities have pursued expanded coverage and financial viability primarily via household connections, which are typically used to subsidize the cost of standpipes. Almost no urban utilities have pursued standpipes as a primary mode of expansion of coverage.<sup>**5**</sup> The percentage of unconnected people covered by standpipes is substantially higher for countries with higher household connection rates. In countries with medium to high household connection rates, 71 percent of the unconnected population relies on standpipes, on average. In countries with low to medium household connection rates, 48 percent of the unconnected population relies on standpipes, on average; and in countries with very low household connection rates, 32 percent of the unconnected population relies on standpipes, on average. The very existence of a relatively expanded water network has spillover effects, allowing better access through standpipes.

The prevalence of the informal market is directly linked to the household connection rate. Not surprisingly, the DHS data show that the percentage of unconnected people covered by water tankers or water vendors is higher for countries with lower household connection rates. Countries with very low household connection rates have 13 percent of their urban unconnected population that relies on either water trucks or water vendors, on average. In countries with low-medium household coverage rates, 4 percent of the urban unconnected population relies on either water trucks or vendors, on average; for countries with medium-high household coverage rates, only 2 percent of the urban unconnected population relies on either water trucks or vendors, on average.

# Standpipes

Standpipes represent the main source of water for unconnected households for most cities. Therefore, it is particularly important to take them into account if one is to understand not only the characteristics of water coverage but also the dynamics of the water market and supply chains. Average standpipe coverage in the cities studied is 28 percent, but standpipe coverage can provide up to 53 percent of the water supply for the unconnected households (table 1.3). These results are very much in line with the widespread belief that standpipes are the main water source for the urban poor and that the poor are likely to comprise a

> 5 In recent projects in urban Burundi, there is a greater focus on standpipes, largely because of constraints in bulk water supply and capital financing.

5
Figure 2: Low earners, especially women, get biggest % increment from public pillar

# **%** increment to own-annuity from public pillar-4ifetime benefits

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66
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**The World Bank and Governance: The Bank’s Efforts to Help Developing Countries Build State Capacity**

Alain de Janvry and Jean-Jacques Dethier<sup>1</sup> JEL codes: N40, O19, P33 Keywords: Development policy; Governance; Capacity Building; Aid effectiveness

> 1 Alain de Janvry (alain@berkeley.edu) is Professor at the University of California, Berkeley, Department of Agricultural and Resource Economics, and Goldman School of Public Policy. Jean-Jacques Dethier (jdethier@worldbank.org) is Research Manager, World Bank and Adjunct Professor, Georgetown University Public Policy Institute.  This paper was presented at the Conference on _Using History to Inform Development Policy. The Role of Archives_ , organized by the World Bank, Washington, D.C. October 25-26, 2012.
especially by humanitarian agencies responding to food crises (Schnitzer, 2019). While different variations of this method also exist, it usually categorizes households into wealth groups based on specified criteria that are agreed by the community and guided by a previous, nationwide qualitative assessment of household livelihoods and food security – the ‘HEA baseline.’ Three of the CBT schemes that we assess rely on such type of CBT.

While targeting is not a recent topic in the economics literature,<sup>17</sup> empirical studies based on household survey data have emerged in recent years with the increasing popularity of social safety-net programs. A few studies have compared PMT and CBT efficiency directly and found that PMT is better at selecting households with low per-capita consumption in Indonesia, Cameroon, and Niger (Alatas et al., 2012; Stoeffler et al., 2016; Premand & Schnitzer, 2020). However, in Indonesia, CBT can generate greater community satisfaction, while the opposite result was found in Niger. Two review studies have measured the performance of PMT, CBT and other targeting methods worldwide (Coady et al., 2004; Devereux et al., 2017). Both found important variations within methods and suggest that this result is explained by important variations in implementation. These studies understate the role of widely diverse levels of coverage rates in explaining targeting performance.<sup>18</sup> While the evidence on PMT and CBT methods in the Sahel region was scarce, several studies were conducted on the subject in the past years as safety nets interventions became increasingly important. We reviewed the existing evidence based on the reports from these studies (review available upon request). Most of these studies cannot be directly compared, given variations in performance measurement, but the overall findings are largely consistent. The following conclusions emerged: (i) PMT selects households with lower per-capita consumption than CBT, but no method makes a large difference; (ii) targeting transparency and appropriation is low regardless of the method employed; (iii) hybrid methods do not work well in comparison to each individual method; (iv) little is known about the legitimacy of different methods and about the role of targeting in the overall program effectiveness.

Our study contributes to the scarce literature that both compares different methods and makes comparisons across countries in a harmonized manner, by directly analyzing household data collected from various programs implemented in the Sahel.

> 17 See for instance Besley & Kanbur (1991), Sen (1992) or Cornia & Stewart (1993) for earlier studies.

> 18 By focusing on the Sahel region and adjusting selection threshold rates when possible, we control for differences in context and coverage rates – and do not find large within-targeting method variations.

11
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_Table 9: Loan commitnents on research components as a percent of Bank Project loans, by region, 1982-1989_

|Region|Total<br>loan<br>commitment<br>(US$ Millions)|loan<br>commitments<br>to research<br>components<br>(US$ Millions)|Percent of<br>total<br>loan<br>commitment<br> <br>(%)|Percent of<br>total loan<br>commitment<br>weighted by<br>project size|
|---|---|---|---|---|
|Africa|597|17.1|2.9|3.2|
|Asia|2040|44.0|2.2|2.6|
|LAC|563|18.2|3.2|4.2|
|EMENA|1260|19.2|1.5|2|
|All regions|<br>4460|98.5|2.2|3.0|

27. Cost olr study. On average, $US 230,000 was budgeted per planned study. However, it is not possible to calculate actual costs per completed study for several reasons. First, the costs of completed studies may be under-estimated from existing information: studies may be partially or completely funded by the govemments and therefore the costs unreported; studies listed as incomplete may have been cancelled and resources reallocated to another sector within the project; extra studies, not reported in SARs, may have been completed using available resources. Secondly, commitments to research outlined in cost profiles of SARs may not be so easily identifiable in disbursement allocations. Many studies are incorporated into line items for technical assistance, because consultant fees are often the main component. This combined with co-financing by other donors and governments hinders information on separate costs per study.

28. One possible method to identify cost per study (assuming that the number of studies completed is correctly counted) is to examine the disbursable amounts allocated to research components. These figures come from the Loan and Trust Funds Department and represent the agreed disbursable amount available through the loan. Comparisons between budgeted support for research components and the allocated disbursable amounts are available for a small sample of African countries. Wide discrepancies are observed. For example, in the 1988 Burundi Education Sector Development Project (CR 1881-BU), there were no studies listed within the SAR, and so nothing was budgeted; but $150,000 was disbursable toward 'education related studies." Unfortunately, data on disbursable amounts are available for only a few projects.

12
# **Table 2: Imports and Exports of The Philippines by Region and Sector of our Model**

||**China and**<br>**Hong Kong**|**Japan**|**India USA**<br> <br> <br>|**Korea**<br>**Australia and**<br>**New Zealand**|<br> <br>**Rest of**<br>**ASEAN**|**Rest of**<br>**World**|**China and**<br>**Hong Kong**|**Japan **|**India USA**<br> <br> <br>|**Korea**<br>**Australia an**<br>**New Zealan**|**d**<br>**d**<br>**Rest of**<br>**ASEAN**|**Rest of**<br>**World**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|||||||||||||
|Wheat and cerialgrains|0.001||0.006 0.672|0.367|0.018|0.126|0.000|0.000|0.000 0.000|0.000|0.000|0.001|
|Agriculture and Forestry|0.329|0.011|0.038 0.118|0.046|0.192|0.330|0.536|0.962|0.003 0.233|0.349|0.088|0.620|
|PrimaryEnergyand Minerals|0.043|0.002|0.004 0.013|0.651|1.802|8.103|1.905|0.401|0.007 0.001|0.282|0.036|0.051|
|Food Products|0.587|0.034|0.058 0.551|0.164|1.635|0.972|0.174|0.311|0.008 1.273|0.129|0.326|0.791|
|Processed Rice|0.000|0.000|0.007 0.000|0.000|0.613|0.019|0.002|0.001|0.000 0.003|0.001|0.001|0.019|
|Meat and Dairy|0.036|0.000|0.111 0.333|0.685|0.047|0.427|0.005|0.032|0.001 0.009|0.004|0.052|0.074|
|Sugar|0.000|0.000|0.000 0.001|0.006|0.133|0.001|0.016|0.052|0.000 0.276|0.021|0.055|0.009|
|Beverages and Tobacco|0.185|0.001|0.003 0.111|0.086|0.170|0.127|0.010|0.004|0.000 0.012|0.077|0.132|0.036|
|Petroleum and Coal Products|1.897|0.132|0.007 0.019|0.680|1.385|0.845|0.078|0.004|0.002 0.014|0.027|0.462|0.364|
|Tex�les and Apparel|1.967|0.131|0.049 0.041|0.271|0.294|0.467|0.196|0.133|0.004 1.331|0.072|0.050|0.678|
|Chemicals Mineral and Metalproducts|3.883|2.952|0.350 0.573|2.551|3.805|3.818|1.634|1.526|0.116 0.590|0.606|1.757|1.356|
|Electronic equipment|1.813|2.258|0.015 1.717|2.108|3.257|4.384|8.684|1.164|0.139 3.230|1.397|3.124|6.376|
|Other manufacturing|4.929|5.360|0.261 1.111|1.494|4.332|3.546|1.605|3.111|0.168 2.594|0.494|1.989|3.071|
|U�li�es|0.001|0.000|0.000 0.002|0.001|0.001|0.010|0.001|0.000|0.000 0.001|0.000|0.000|0.007|
|Other Services|0.037|0.015|0.004 0.253|0.039|0.027|0.524|0.063|0.049|0.012 0.126|0.055|0.047|0.840|
|Trade|0.415|0.005|0.003 0.046|0.017|0.020|0.240|0.048|0.028|0.009 0.095|0.040|0.031|0.568|
|Transport nec|0.038|0.009|0.016 0.036|0.014|0.027|0.453|0.141|0.082|0.026 0.298|0.123|0.094|1.713|
|Water Transport|0.006|0.002|0.001 0.005|0.003|0.008|0.069|0.013|0.021|0.006 0.022|0.019|0.015|0.233|
|Air Transport|0.044|0.029|0.003 0.105|0.032|0.052|0.592|0.091|0.116|0.032 0.358|0.117|0.082|1.402|
|Communica�on|0.006|0.002|0.004 0.023|0.004|0.008|0.145|0.024|0.012|0.008 0.083|0.021|0.031|0.545|
|Financial services nec|0.020|0.008|0.005 0.108|0.009|0.016|0.330|0.016|0.010|0.004 0.042|0.013|0.011|0.243|
|Insurance|0.006|0.005|0.004 0.055|0.005|0.009|0.191|0.006|0.003|0.001 0.020|0.003|0.004|0.066|
|Business services nec|0.065|0.027|0.052 0.160|0.034|0.060|0.903|0.097|0.145|0.089 0.364|0.103|0.137|2.393|

# **Source: GTAP 9 Database**

49
# **7 Appendices**

# **7.1 Estimation results, mover-stayer model**

## **Mover-stayer model for heads, Logit estimates**

Number of obs = 6726868.000 Wald chi2(6) = 197.120 Prob > chi2 = 0.000 Pseudo R2 = 0.068 Log pseudo-likelihood = -655384.78

||_Coef._<br>_Ro_|_bust Std._<br>_Err._|_z_|_P>|z|_|_[95% Co_|_nf. Interval]_|
|---|---|---|---|---|---|---|
|dedu|0.419|0.278|1.510|0.132|-0.126|0.963|
|age|-0.044|0.005|-9.490|0.000|-0.053|-0.035|
|ddworkown|-1.123|0.319|-3.520|0.000|-1.749|-0.497|
|dcess|-0.825|0.327|-2.530|0.012|-1.466|-0.185|
|dprop|-0.736|0.172|-4.280|0.000|-1.074|-0.399|
|dgregio1|0.593|0.202|2.930|0.003|0.196|0.990|
|_cons|-1.708|0.178|-9.580|0.000|-2.058|-1.359|

## **Changes in Predicted Probabilities for Moving out of Agriculture**

||_0->1_|_-+sd/2_|_MargEfct_|
|---|---|---|---|
|dedu|0.008||0.007|
|age||-0.011|-0.001|
|ddworkown|-0.012||-0.018|
|dcess|-0.009||-0.013|
|dprop|-0.010||-0.012|
|dgregio1|0.012||0.009|

Source: Authors’ calculations. Note: dedu: educational dummy for 10 or more years of schooling ddworkown: dummy for own-consumption workers dcess: holding ceded land dprop: holding own land dgregio1: north.

56
**Table 3.11: Index of Real Wages per Hour by Affiliation to Social Security** **<u>(2007=100)</u>**

||2007|2008|2009|2010|
|---|---|---|---|---|
|IMSS/Salaried|100|98|94|91|
|ISSSTE/Salaried|100|99|97|97|
|PEMEX—Defense/Salaried|100|94|89|87|
|Not affiliated—employer/Self employed|100|98|92|90|
|Not affiliated/Salaried|100|99|96|94|

**Source: Own calculations using data from ENOE.**

All this evidence reveals the asymmetry of adjustment over the course of the crisis. During the recession, some sectors reduced employment quantities only, others adjusted real wages only, and others adjusted the two margins. During the recovery, all sectors (with the exception of mining) have showed job creation with lower real wages (see figure 3.1). The crisis produced a significant dislocation of the Mexican labor market. There were large flows of job destruction and creation that left the country with employment levels similar to before the crisis, but unemployment levels of around 900,000 people more than before the crisis. Average wages declined for all sectors

# **4. Policies to Cope with the Crisis**

The Mexican government announced a series of programs to cope with the crisis, which included the Program to encourage Growth and Employment (PICE) announced in October 2008, and the National Agreement to support the Household Economy and Employment ( _Ampliación del Programa de Empleo Temporal a Nivel Federal_ —ANFEFE), announced in January 2009. The expected outcome was a about a 0.9 percent boost of GDP. Additional support in the form of credit from development banks was expected to increase total investment to about 171,000 million pesos, or about 1.4 percent of GDP (CEFP, 2009).

PICE aimed to cope with the effects of the economic crisis by using resources mainly from oil revenues. A total of 255,000 million pesos was to be used in infrastructure, massive transport programs, and other priority sectors. These goals were reinforced in January 2009 with a presidential agreement creating the Intersecretarial Commission for Acquisitions and Works in the Public Administration for the Small, Micro and Medium Firms. The commission aimed to ease crisis conditions by at least a 20 percent increase in public sector acquisitions from those firms.

The main mechanism of PICE was public expenditure in infrastructure, with an initial budget of about 0.7 percent of the GDP (excluding investment in oil sector) of about 90,000 million pesos. The National Fund for Infrastructure is part of PICE which is the flagship infrastructure program of the administration. Another 90,200 million pesos was awarded to PEMEX to build a new refinery and other infrastructure. Despite a lack of information about the execution of those programs, a report of the House of Deputies (CEFP, 2009) stated that at as of mid-2009, there had been very little progress. For example, by the first quarter of 2009 only 15 percent of the infrastructure fund 33
**Table 2. Baseline and Alternative Assumptions**

||1995-<br>2010|2011-<br>2050|2051-<br>2100|
|---|---|---|---|
|Real wage growth-baselinea|5.5%|3.0|3.0%|
|Slow growth|1.0%|1.0%|**_1.0%_**|
|Real interest rate-baseline|0.0%|4.0%|4.0%|
|Past reform|3.0%|4.0%|4.0%|
|Slow reform and slow growth|0|1.0|1.0|
|Noncompliance<br>+ exemption rate|25%|25%|25%|
|Benefit rate (average pension/ave.<br>age)||||
|<br><br>baseline|80t|80%|80%|
|Reform|60%|60%|**_609_**|
|Replacement<br>rate of final year wage||||
|baseline|73%|73%|73t|
|Reform|55%|55%|55%|
|Retiremeint age-<br>M|60|60|60|
|- F|55|55|55|
|Reform<br>(at end of period)-<br>M|60|65|65|
|-<br>F|60|65|65|
|Rate of increase per year in|4 months|2|2|
|reform||months|months|
|Indexation<br>to real wage-baseline|50%|50%|50%|
|Reform<br>(price indexation)|0|0|0|
|Coverage rate-<br>urban|72W|72%|72%|
|Increased urbanization-baseline|0|0|0|
|In-migration<br>rateb|1.5%|1.0%|0|
|TVE + private share of rural labor|28%|50%|70%|
|(at beginning<br>of period)||||
|TVE+rural private coverage|0|0|0|
|<br>Alternative-at<br>beginning of periodc|30%|54%|80%|
|Increase per year in TVE labor share &|1.5%|1.5%|0|
|coverage rate (percentage points)||||
|Ratio--rural<br>to urban wage|70%|70%|70%|

In addition to this rate of economy-wide wage growth, which applies to all cohorts, workers are assumed to receive a 1% per year wage increase for experience during their first thirty years of employment. This generally does not affect the average wage growth in the economy or the ratio of average pension to average wage (the benefit rate) but it does affect ratio of pension to final year's wage for the average retiree (the replacement rate).

Only applies to population under age 35. Gives % increase in urban share of total population in each group under 35. (eg. if the urban share was 30% in 1995 it becomes 30.45% in 1996) Covered TVE workers are eligible for pensions beginning with those who retire in 2011.

**38**
theories and at least 145 proposed regressors as proxies. We turn to the standard growth literature to guide us in this regard and focus on a subset of well accepted growth determinants in our analysis (see annex tables and data description for details).

The baseline empirical growth model consists, in addition to the variable of interest, of four determinants of economic growth - initial income level, rates of physical and human capital accumulation, trade openness and institutional quality. We use data on income per capita from WDI. To account for differences in human capital accumulation we use latest available data on years of schooling from Barro and Lee (2010).<sup>11</sup> We use M2 (as percent of GDP) as a proxy for financial development from WDI. Finally, we use a measure of Rule of Law from World Governance indicators.<sup>12</sup> We use dynamic panel regressions based on three year panel data for each country. The regression specification is the following: where is the natural logarithm of GDP per capita, is the level of GDP per capita at the beginning of each three year period, is the dynamic service export sophistication measure, is the set of relevant control variables for growth determinants, represents time dummies, stands for country fixed effects and is the error term.

Results of fixed effect and System GMM regression for three year periods are presented in Table 3.<sup>13</sup> The convergence effect, human capital, and service EXPY seem to matter for growth only in the last time period (2005-07). We add various controls for growth to our baseline regression for the period. Introduction of the controls does not change the significance of the co-efficient on _SE_ .<sup>14</sup>

> 11 Neoclassical growth model, since the seminal work of Lucas (1988), the concept of capital since is usually broadened from physical capital to include human capital.

> 12 For robustness checks we also tried institutionalized democracy measure from Integrated Network on Social Conflict Research which has a value between 0 and 10. This is a composite index which includes information on degree of participatory democracy, the existence of institutionalized constraints on the exercise of power by the executive, and other aspects of plural democracy, such as the rule of law, systems of checks and balances, and freedom of the press. Our results do not change.

> 13 By using shorter time spans we are left with a sample of approximately 100 countries by 2007. We have excluded few countries where the data on service exports was reported haphazardly for a limited time period; hence we only keep reliable data for panel estimations. 14 Initial, lagged levels are used to address potential endogeniety problems (see for example Barro and Lee (1994))

18