rafmacalaba/datause-encoder-data huggingface dataset:
education’, the maximum education of father and mother. Since the vast majority of parents in the sample are not current students, we keep parental education constant across waves. On average, both parents and household heads have attended primary school, but about 20 percent are still illiterate. The other characteristics of household head we analyzed are age, gender, and marital status. Household heads are relatively young, averaging 45 years in baseline, 47 in the second wave, and about 49 in the third. More than 70 percent of household heads are married; the gender balance of headship is skewed to males, with only about 25 percent of households headed by females.

Apart from individual and household head characteristics, the effects of assets may differ by income level, rural or urban location, household response to transitory shocks, and access to a school in the local community. Apparently, even though more than 70 percent of households in the sample are rural, a strikingly large proportion (90 percent) have a primary or secondary school in the village. Access to primary and secondary schools in rural areas signals that the country has made a large investment in educating children (though the quality of education is not known). Descriptive results also indicate growing resilience over time: though 78 percent of households at some point experienced a negative economic shock, the poverty rates did not go up. Consistent with the reduction in national poverty rates (World Bank, 2015), average annual household consumption increased from T Sh2.5 million in the baseline to T Sh3.8 million in the third wave.

Table 3 summarizes children’s educational outcomes. Because educational outcomes are not available for children who have never attended school, both our summary statistics and the empirical results are conditional on attending school. We track the cohort of children aged 6-18 at baseline to estimate the effect of assets on ‘highest grade completed’. On average, children in the sample had completed 5<sup>th</sup> grade at baseline, 7<sup>th</sup> grade in the second wave, and 9<sup>th</sup> grade in the third. As PSLE and FIVE data are not available for the first wave, we use only the PSLE and FIVE data from the second and third waves. Even though in both waves the passing rate for both tests is higher than 65 percent, 17
<!-- Start of picture text --> 40<br>a5<br>30<br>25<br>20<br>is<br>10<br>= ——W ith China ——W ithout China<br>oO<br>1960/1961 1s7o/197. 1980/1981 1990/1991 2000/2001 2010/2011<br><!-- End of picture text --> Source: US Department of Agriculture
<!-- Start of picture text --> \<br>rela<br>:<br>‘8<br>biot<br>162.4 - 199.8<br>199.9 - 208.5<br>Ml 208.6 - 219.0<br>MH 219.1 - 234.6<br>MB 234.7 - 264.0<br><!-- End of picture text --> <!-- Start of picture text --> }<br>; &<br>q<br>* ’ a<br>bio4 ' }<br>1350 - 1921<br>1922 - 2197<br>Ml 2198- 2415<br>Ml 2416- 2670<br>Ml 2671 - 3015<br><!-- End of picture text -->
31 Fay, M., D. Leipziger, Q. Wodon and T. Yepes (2003), Achieving the Millennium Development Goals: The Role of Infrastructure, Policy research working paper 2390, Washington, D.C.

Government of Vietnam (2001), “Infrastructure and the MDGs” Government-NGO poverty - task force, available at http://www.vdic.org.vn/eng/pdf/infra <u>eng.pdf</u> Jayasuriya, R. and Q. Wodon (2003), “Efficiency in Reaching the Millenium Development Goals”, World Bank Working Paper, No9 Komives, K. & Whittington, D. & Wu, X. (2001), "Infrastructure Coverage and the Poor: A Global Perspective," World Bank, Policy Research Working Papers, No 2551.

Manning, R. (2004), How can the development community help achieve greater progress towards the MDGs”, WIDER public lecture, February 9, 2004 Ravallion, M. (2003), The Debate on Globalization, Poverty, and Inequality: Why Measurement Matters”, World Bank, Policy Research Working Papers, No 3038 Sachs, J. (2004), “Doing the Sums on Africa”, The Economist, May, available at: <u>http://www.undp.org/mdg/Economist_May%2020%202004.pdf</u> United Nations (2001-2004), Country Reports on MDGs, available at: <u>http://www.undp.org/mdg/country_regionalreports.html</u> Wang, L. (2002). “Health Outcomes in Poor Countries and Policy Implications: Empirical Findings from Demographic and Health Surveys _”,_ World Bank, Policy Research Working Paper 2831

# **Bibliography for Financing needs**

Briceno, C., A. Estache and N. Shafik (2004), “Infrastructure Services In Developing Countries: Access, Quality, Costs and Policy Reform”, The World Bank, (forthcoming) Chatterjee, S.; Sakoulis, G.; Turnovsky, S.(2003), “Unilateral Capital Transfers, Public Investment, and Economic Growth”, _European Economic Review_ , 47 (6), pp. 1077-1103 Coelli, T., A. Estache, S. Perelman and L. Trujillo (2003), **A primer on efficiency measurement for utilities and transport regulators** , WBI Development Studies, Washington, DC Easterly, W. and L. Serven, editors (2003), **The Limits of Stabilization** , Stanford University Press, Stanford Fay, M. and T. Yepes (2003), “Investing in Infrastructure: What is needed from 2000-2010”, World Bank, Policy Research Working Paper, 3102 Flyvbjerg, B. M. Skamris Holm and S. Buhl, (2003), “How common and how large are cost overrun in transport infrastructure projects”, _Transport Review,_ Vo. 23, 1, 71-88 Flyvbjerg, B, N. Bruzelius and W.Rothengatter (2003), **Megaprojects and Risk: An Anatomy of Ambition,** Cambridge Univiesry Press IEA (2003), World Energy Outlook, Paris Kessides, I. (2004), **Reforming Infrastructure: Privatization, Regulation, and Competition** , Oxford University Press United Nations (2004) , http://www.unmillenniumproject.org/html/docs%20and%20reports.shtm.] United Nations (2004) http://www.unmillenniumproject.org/html/secretariatdocs.shtm.
**Table 3: Dwell time in Sub-Saharan Africa**

|Durban|Douala|Lome|Tema|Mombasa|Dar-es-<br>Salaam|**Average**<br>**(Durban**|
|---|---|---|---|---|---|---|
|South<br>Africa|Cameroon|Togo|Ghana|Kenya|Tanzania|**excluded)**|
|4|19|18|20|9|19|**17**|

_Source: Refas et al (2011) for Douala, surveys for the other ports._ When cargo dwell time is broken down between operational, transactional and discretionary storage<sup>10</sup> , it appears that Durban favorably compares with Mombasa and even more so with Dar-esSalaam<sup>11</sup> . The most significant difference concerns clearance procedures, where up to 6 days can be gained, as well as for discretionary storage. The last factor seems to play a major role and is therefore discussed in detail below.

> 10 Operational dwell time is mainly the time to unload vessels and store in yards. It mainly depends on the efficiency of the port and the availability of equipment combined with the level of occupancy of the storage facilities. Transactional dwell time mainly concerns the transaction time between the importers/port services and customs. Discretionary storage is the residual after having taken into account operational and transactional dwell times.

> 11 In an ideal situation, the minimum time for the transactions is 1 day for clearing the cargo depending on whether a physical inspection is required. Another 1-2 days for the transfer from the vessel to the yard and a similar amount for the transfer from the container yard to the ODCY. Finally it would require half a day/ a day to pay the port and shipping fees and exit the port, which means that mean dwell time can not really go, on average, below 3 days.

9
**Table 2. Marginal effects of the correlation between the availability of childcare and labor market outcomes**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With|individual co|ntrols|With individ|ual + househo|ld controls|With individ|ual + househo<br>controls|ld + regional|
||Earner|LFP in last<br>30 days|Worked in<br>last 30 days|Earner|LFP in last<br>30 days|Worked in<br>last 30 days|Earner|LFP in last<br>30 days|Worked in<br>last 30 days|
|_Ref: A. No child or youngest child is 6 years_||||||||||
|_and older_||||||||||
|B. Youngest child is 0–5 years and|-0.155***|-0.148***|-0.132**|-0.184***|-0.167***|-0.160***|-0.174***|-0.157***|-0.150**|
|<br>childcare available|(0.056)|(0.057)|(0.056)|(0.059)|(0.059)|(0.059)|(0.059)|(0.059)|(0.059)|
|C. Youngest child is 0–5 years and no|-0.313***|-0.281***|-0.315***|-0.311***|-0.278***|-0.315***|-0.305***|-0.273***|-0.309***|
|childcare available|(0.056)|(0.057)|(0.055)|(0.058)|(0.060)|(0.057)|(0.058)|(0.060)|(0.057)|
|Age|0.077***|0.085***|0.078***|0.062**|0.078***|0.065***|0.059**|0.074***|0.062**|
||(0.022)|(0.022)|(0.022)|(0.025)|(0.025)|(0.025)|(0.025)|(0.024)|(0.024)|
|Age squared|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001***|-0.001**|-0.001***|-0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|_Ref: Education: No education_||||||||||
|Class 1 to 5|-0.161***|-0.127**|-0.140***|-0.128**|-0.098*|-0.106*|-0.137**|-0.108*|-0.115**|
||(0.053)|(0.053)|(0.053)|(0.056)|(0.055)|(0.056)|(0.056)|(0.056)|(0.056)|
|Class 6 to 8|-0.201***|-0.194***|-0.183***|-0.120|-0.121*|-0.091|-0.110|-0.111|-0.081|
||(0.068)|(0.068)|(0.068)|(0.074)|(0.073)|(0.074)|(0.074)|(0.073)|(0.073)|
|Class 9|-0.182*|-0.070|-0.160|-0.107|0.014|-0.074|-0.112|0.013|-0.077|
||(0.100)|(0.100)|(0.099)|(0.108)|(0.107)|(0.107)|(0.106)|(0.107)|(0.106)|
|SSC and above|-0.118|-0.144*|-0.135|-0.021|-0.040|-0.032|-0.037|-0.060|-0.048|
||(0.086)|(0.086)|(0.087)|(0.099)|(0.098)|(0.100)|(0.100)|(0.100)|(0.102)|
|Wear burkha to travel outside|-0.104**|-0.128***|-0.101**|-0.086*|-0.104**|-0.079|-0.080|-0.097*|-0.075|
||(0.049)|(0.048)|(0.049)|(0.050)|(0.050)|(0.050)|(0.051)|(0.050)|(0.051)|
|Head’s age||||-0.001|-0.000|-0.000|-0.001|0.000|0.000|
|||||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Head is female (=1)||||0.283***|0.297***|0.241***|0.289***|0.307***|0.250***|
|||||(0.084)|(0.085)|(0.085)|(0.086)|(0.087)|(0.087)|
|_Ref: Head’s education: No education_||||||||||
|Class 1 to 5||||-0.134**|-0.114**|-0.127**|-0.140**|-0.121**|-0.132**|
|||||(0.056)|(0.055)|(0.056)|(0.057)|(0.056)|(0.056)|
|Class 6 to 8||||-0.154**|-0.092|-0.150**|-0.158**|-0.095|-0.151**|
|||||(0.066)|(0.065)|(0.066)|(0.066)|(0.065)|(0.066)|
|Class 9||||<br>0.045|<br>0.046|<br>-0.002|<br>0.024|<br>0.021|<br>-0.020|
|||||(0.113)|(0.114)|(0.113)|(0.112)|(0.112)|(0.113)|
|SSC and above||||-0.157|-0.156|-0.143|-0.163|-0.163*|-0.150|
|||||(0.100)|(0.097)|(0.100)|(0.102)|(0.098)|(0.101)|
|HH receive international remittance=1||||-0.343**|-0.334**|-0.291**|-0.313**|-0.305**|-0.262*|
|||||(0.144)|(0.146)|(0.143)|(0.148)|(0.150)|(0.148)|

14
32 adjusted MDG intervention coverage, which may reflect in some cases a widening gap between the poor and better off (Wagstaff et al. 2014). While, as we mentioned in the Introduction, we cannot conclude anything about causal effects of reforms from Figure 4, it is nonetheless striking that in some of the countries that have implemented MDG-focused UHC-inspired reforms, such as Ethiopia (2003) and Nigeria (2008), we do not see striking and sustained improvements in inequalityadjusted MDG intervention coverage following the reforms. By contrast, we _do_ see evidence of progress in countries where MDG indicators have _not_ been the focus of UHC-inspired reforms. This suggests that broader-based reforms (which have often been associated with demand-side measures) may have had beneficial effects also on MDG indicators. We have less data for the non-MDG indicators: questions on inpatient admissions and cancer screening are less common in household surveys than questions concerning MDG interventions. We see an encouraging upward trend in Argentina, Colombia, and Peru, and an increase in non-MDG intervention coverage in Chile after the launch of _Plan AUGE_ ( _Acceso Universal de Garantías Explícitas_ ) _,_ which aimed at ensuring universal coverage of a specific set of interventions; but we see unsteady progress in Guatemala and Mexico.

Trends in financial protection have been even less clear-cut. In some countries, such as Argentina, Ethiopia, Guatemala, Thailand, and Turkey, both domains of financial protection have shown improvements over time. It is hard to believe that the changes in Ethiopia have anything to do with the recent reforms given the reforms’ focus on MDG interventions. The data for Turkey and Thailand postdate 32
quality and lower costs are more likely to pursue this bundle of activities. However, neither coefficient is significant, nor are the coefficients for the other bundles, so the case for causation from previous performance to activity choices is weak. Table A2.4 reports analogous results for Morocco, where we observe data on exporting and foreign equity holdings. The results are broadly similar to those in Colombia. Activities are persistent, however, the degree of persistence as measured by the magnitude of the coefficients on own lagged activities is generally smaller. This is especially true for FDI, where past FDI activities raises the probability of current FDI activity by only 10 percent in the first set of regressions, and not at all in the second set including performance variables (which are reported only for plastics).

One puzzling exception to the pattern of insignificant effects of performance on the choice of activities is in the equation for the choice of both activities. Other things equal, firms with higher quality and lower costs are _less_ likely to choose both activities. This mirrors the negative correlation between product quality and joint pursuit of exports and FDI found in tables A1.2 and Al.5. Taken at face value, it suggests that foreign investors are attracted to plants that have been performing relatively poorly.

# **IV Summary**

This paper has several basic messages. First, by imposing enough structure on the production function and the demand system, it is possible to measure product quality and marginal costs at the plant level and to relate the evolution of these variables to firms' activity histories. Doing so, we find strong firm-level persistence in both quality and

> the increase from K=2 to K=3 activities would result in 144 parameters to be estimated with at most 580 observations in the plastics industry.

32
# **Chart 4. Implications of Alternative Proposals: Change in emissions growth compared to business-as-usual  (annual average % between 2010 and 2050)**

<!-- Start of picture text --> A. Equal per capita emissions<br><!-- End of picture text --> <!-- Start of picture text --> NGA<br>PAKVNMIND PHL<br>IDNMAR<br>UZB EGY COL<br>IRQ CHN BRA<br>DZATHA<br>UKR TUR<br>ROMARGMEXCHL<br>ZAFIRNBLRVEN<br>MYS<br>KAZ<br>POL<br>RUS SAUCZEKORISRGRCITAESPJPNFRADEUGBRBELAREAUSCANAUTKWTNLDUSASGP<br>HKG<br>7 8 9 10 11<br>Log of GDP (PPP) per capita, 2008<br>10<br>0<br>-10<br>Percent<br>-20<br>-30<br><!-- End of picture text --> <!-- Start of picture text --> B. Historic responsibility<br>NGA<br>VNMIND PHL<br>PAK IDNMAR COLBRA<br>EGY<br>CHN THA ROMTURCHL<br>ARGMEX<br>DZA FRA<br>UZB IRQ UKR BLRVEN ITAESP<br>ZAFIRN MYS POL GRCJPNGBRAUT SGP<br>KOR DEU<br>KAZ RUS CZEISR BELNLD<br>SAU HKG<br>CAN<br>AUS<br>USA<br>ARE<br>KWT<br>7 8 9 10 11<br>Log of GDP (PPP) per capita, 2008<br>0<br>-5<br>Percent -10<br>-15<br>-20<br><!-- End of picture text --> 23
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 15. Liberia is beginning to capture significant amounts of development aid**

Investment in infrastructure sectors as percentage of GDP <!-- Start of picture text --> WSS transport communications energy<br><!-- End of picture text --> _Source_ : World Bank 2010.

# How much more can be done within the existing resource envelope?

Even without increasing spending, more resources could be directed towards infrastructure by eliminating inefficiencies. The largest potential source of efficiency gains is improving cost recovery. In the road sector, for example, charging the a fuel levy designed to permit full cost recovery of maintenance expenditures—around $0.25 per liter—could raise $9 million annually for road maintenance. Setting the fuel levy that high probably would not be feasible due to the impact on end-user prices for fuel. So in practice only about half of this amount could be recovered through road user charges. Despite today’s elevated tariffs, the power sector still benefits from an annual operating subsidy of $2.3 million a year. About half of that total was covered by a capital grant for power sector development from donors, but the remaining half constitutes an ongoing operating subsidy. Once again, however, it may not prove feasible to raise power tariffs further above their existing level, even if it could be argued that in today’s circumstances power is a luxury in Liberia that should be priced at full cost. Finally, the water and power utilities could recoup some $2.9 million each year by reducing distribution losses and increasing revenue collection. Overall, around $17 million of additional resources could be recovered each year by improving efficiency (table 15).

29
Table 7: Local spillovers on employment and independent activities

||(1)<br>Employment<br>(Has an<br>activity of<br>any type)|(2)<br>Self<br>employed<br>(at least<br>1 activity)|(3)<br>Wage<br>employed<br>(at least<br>1 activity)|(4)<br># Independent<br>activities|(5)<br>Total<br>hours<br>worked<br>(last 7<br>days)|(6)<br>Hours<br>worked in<br>self<br>employment|(7)<br>Hours<br>worked in<br>wage<br>employment|
|---|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates||||||||
|Treatment (ITT)|0.01|0.01|-0.01|0.37**|-2.10|-0.60|0.19|
||(0.01)|(0.02)|(0.02)|(0.15)|(1.88)|(1.37)|(0.73)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|97.9%|93.2%|12.4%|3.23|44.11|9.32|2.83|
|Observations|1,102|1,102|1,102|1,102|1,102|1,102|1,102|
|Panel B. Treatment Arm Estimates||||||||
|VSLA (T1) (ITT)|-0.00|0.00|0.01|0.40**|-2.54|-2.76|0.44|
||(0.01)|(0.03)|(0.03)|(0.18)|(2.29)|(1.67)|(0.96)|
|Cash Grant with repayment (T2) (ITT)|0.01|0.02|-0.03|0.29|-2.60|0.93|-0.24|
||(0.01)|(0.02)|(0.03)|(0.19)|(2.33)|(1.83)|(0.91)|
|Cash Grant (T3) (ITT)|0.02*|0.01|-0.01|0.50**|0.09|0.01|0.75|
||(0.01)|(0.02)|(0.04)|(0.22)|(2.44)|(1.43)|(1.30)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|97.9%|93.2%|12.4%|3.23|44.11|9.32|2.83|
|p-value T1=T2|0.35|0.56|0.20|0.57|0.98|0.06|0.54|
|p-value T2=T3|0.48|0.77|0.61|0.37|0.29|0.61|0.47|
|p-value T1=T3|0.17|0.77|0.61|0.66|0.30|0.08|0.82|
|p-value T1=T2=T3|0.38|0.84|0.44|0.66|0.48|0.12|0.73|
|Observations|1,102|1,102|1,102|1,102|1,102|1,102|1,102|

Robust standard errors clustered at locality level. Hours worked are winsorized at the 99th percentile. Hours (columns 5-7) are measured for the last 7 days for the three main activities of the individual. The number of independent activities per individual is based on all activities operating in the last 12 months. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
Dykstra, Dykstra, and Sandefur (2014), by automatizing the sourcing of results from the World Bank website, collected 10,000 points from the underlying distribution of income or consumption for each country and each year in the _PovcalNet_ database. The resulting data set is, using the words of the authors, _a data set of distributions, not the original micro survey data_ (Dykstra, Dykstra, and Sandefur, 2014, p.1).<sup>5</sup> We use data from the most recent year available for 129 countries featured in the database. Only for 10 countries in the data set is the available survey older than 2000, while for 44 countries it is from 2010 or more recent. The countries represented in the data set cover all income groups and regions in the word. Appendix A summarizes the list of countries and corresponding years.

# **5 Is poverty reduction feasible through DRM? The facts**

## **5.1 Arithmetics**

The interaction between poverty level, optimal marginal tax rate required to eliminate poverty, and the political feasibility is not straightforward. The empirical relation between headcount poverty and the optimal marginal tax rate on the non-poor to eradicate poverty is positive and increasing: the higher the share of population below the poverty line, the higher the marginal tax rate that needs to be levied on the excess-poverty gaps of the nonpoor in order to eradicate poverty. Nevertheless, the same poverty rate may correspond with very different patterns in the Poverty Eradication Capacity Curve. Take, for example, the cases of Namibia and Senegal (Figure 4).

The two countries have about the same share of individuals living below the poverty line of 2 dollars per day in 2005 purchasing power parities (51 percent in Namibia and 55 percent in Senegal). On the other hand, they differ very much with respect to the arithmetics of their redistribution capacity. Namibia’s poverty eradication capacity curve lays above Senegal’s regardless of the redistribution rule (whether the non poor, left panel, or the rich, right panel, are taxed), which means that for any possible marginal tax rate on excesspoverty (or excess-affluence) gaps, Namibia will be able to compensate a larger share of poverty gaps than Senegal. When the society considers all non-poor individuals responsible for poverty eradication, the optimal marginal tax rate is about 16 percent in Namibia and 5 We are aware of the disclaimer posted on _PovcalNet_ which advises against using the tool for tracing out the entire distribution of income or consumption. We are also aware of issues related to the use of secondary databases for distributional analysis (Atkinson and Brandolini, 2001). Nevertheless, the data set made available from the effort of Dykstra, Dykstra, and Sandefur (2014) is an incredible asset to researchers, as it provides an approximation of the welfare distribution for all available countries and available years, in a relatively computationally lean form. We are aware that any results here reported, should be validated by using the underlying microdata, whenever available for the analysis.

14