rafmacalaba/datause-encoder-data huggingface dataset:
_Table 15: Descriptive statistics of Poverty-DRM survey and GLSS6 (GAMA)_

|Household characteristics|DRM|GLS|
|---|---|---|
|HH size|3.44|3.41|
|Age of head|45.60|41.98|
|Number of rooms|1.53|2.03|
|Female household head|44.6%|31.3%|
|Highest level of schoolingcompleted bythe household head|||
|None|13.5%|8.0%|
|Less than primary|8.9%|3.7%|
|Primary completed|10.4%|6.8%|
|JSS/JHS completed|20.6%|18.9%|
|Middle school completed|17.4%|20.3%|
|SSS/SHS completed or above|29.3%|42.4%|
|Proportion of household members employed (question asked<br>to members aged 5 and over)|64.9%|57.5%|

The use of public toilets is significantly higher for slum-dwellers than Accra residents. While 32% of households in the GLSS6 survey reported using public toilets as their main toilet facility, the number for the DRM survey is 67% (see Figure 14). Public toilets are shared pay-per-use toilet facilities within communities. Public toilets are ideally constructed for transient populations and for areas with heavy public activity (WSMP,2008). However, they are also common in slums where households do not have toilet facilities within their housing structure. In Engstrom et al. (2017), the use of public toilets is highly correlated with slum status. We asked community leaders about the most important challenges associated with providing safe sanitation services and they mentioned overcrowding, lack of space in houses to install toilets and affordability. They mentioned examples of landlords making space meant for installing toilets into living areas in order to fit more people in the dwelling. From a service provider’s perspective, they mentioned that the community is planned in a way which makes it difficult to dislodge sewage and other effluent.

The primary source of drinking water in the slum areas is sachet water (Figure 13). Sachet water is purified water purchased by households in small plastic bags and is generally considered a clean and safe source of drinking water. It is however a significant waste management problem due to the amount of plastic waste it generates. This is reflected in the responses from community leaders who also highlight that sachet water is the least affordable option for accessing drinking water in Accra. They also say that even though pipe-borne water is available to households in slum areas it does not always flow. The main challenges in accessing safe pipe-borne water inside the units for households in slums are the nature of structure making separate access difficult. This forces households to share utility bills. They also mention lack of space in the communities to install water pipes. Despite these difficulties highlighted by the local leaders, pipe-borne water sources are reported frequently in the DRM survey.

38
Table 7. Contributions to the Change in Poverty Head Count Ratio- Returns to Characteristics

||Bangladesh,|2000-10|Peru,20|04-2010|Thailand,2|000-2009|
|---|---|---|---|---|---|---|
||Percent|Share of|Percent|Share of|Percent|Share of|
|Contribution to national moderate poverty|point|total|point|total|point|total|
|reduction on account of changes in:|change|change|change|change|change|change|
|**Education**|**2.14**|**-12%**|**0.21**|**-1%**|**-3.29**|**26%**|
|Nonfarm|||||||
|Change in endowment|-0.55|3%|-0.25|2%|-1.34|10%|
|Change in returns to endowment|1.77|-10%|0.54|-3%|-0.28|2%|
|Farm|||||||
|Change in endowment|0.13|-1%|-0.08|1%|-0.56|4%|
|Change in returns to endowment|0.78|-4%|0.00|0%|-1.11|9%|
|**Sector**|**5.58**|**-32%**|**-1.46**|**9%**|**1.71**|**-13%**|
|Nonfarm|||||||
|Change in endowment|-0.48|3%|-0.08|1%|-1.01|8%|
|Change in returns to endowment|5.85|-34%|-1.05|7%|3.50|-27%|
|Farm|||||||
|Change in endowment|||-0.14|1%|-1.11|9%|
|Change in returns to endowment|0.21|-1%|-0.19|1%|0.32|-3%|
|**Age/Gender**|**4.01**|**-23%**|**1.45**|**-9%**|**-2.93**|**23%**|
|Change in endowment|-3.48|20%|-1.17|7%|-1.18|9%|
|Nonfarm|||||||
|Change in returns to age|3.94|-23%|0.97|-6%|-0.87|7%|
|Change in returns to gender|-0.44|3%|0.77|-5%|-0.01|0%|
|Farm|||||||
|Change in returns to age|4.19|-24%|0.96|-6%|-0.54|4%|
|Change in returns to gender|-0.20|1%|-0.08|1%|-0.33|3%|
|**Returns to other characteristics**|||||||
|Urban|||||||
|Nonfarm|0.11|-1%|0.20|-1%|-0.40|3%|
|Farm|0.45|-3%|0.12|-1%|-0.09|1%|
|Regions|||||||
|Nonfarm|-0.87|5%|-4.79|30%|-0.27|2%|
|Farm|-1.82|10%|-0.18|1%|-1.62|13%|
|Land (Farm)|-7.25|42%|-3.18|20%|||
|<br>Irrigation (Farm)|0.30|-2%|-0.11|1%|||
|Other members (Farm)|1.48|-9%|1.07|-7%|||
|Constant|||||||
|Nonfarm|-14.87|86%|-3.38|21%|-2.71|21%|
|Farm|-7.74|45%|0.00|0%|-1.11|9%|

Source: Own estimates based on Peru's ENAHO 2004 -2010, Thailand's SES 2000 -2009, and Bangladesh's HIES 2000 -2010.

34
# **References**

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- **Adhikari, Samik and Ugo Gentilini** , “Should I stay or should I go: Do cash transfers affect migration?,” _World Bank Policy Research Working Paper_ , 2018, (no. 8525).

- **Beegle, Kathleen, Joachim De Weerdt, and Stefan Dercon** , “Migration and economic mobility in Tanzania: Evidence from a tracking survey,” _Review of Economics and Statistics_ , 2011, _93_ (3), 1010–1033.

- **Blattman, Christopher, Eric P Green, Julian Jamison, M Christian Lehmann, and Jeannie Annan** , “The returns to microenterprise support among the ultrapoor: A field experiment in postwar Uganda,” _American Economic Journal: Applied Economics_ , 2016, _8_ (2), 35–64.

- **, Nathan Fiala, and Sebastian Martinez** , “Generating skilled self-employment in developing countries: Experimental evidence from Uganda,” _The Quarterly Journal of Economics_ , 2014, _129_ (2), 697–752.

- **, , and** , “The long term impacts of grants on poverty: 9-year evidence from Uganda’s Youth Opportunities Program,” Technical Report, National Bureau of Economic Research 2019.

- **Brown, Ryan** , “EU tests idea in Gambia: Would more opportunity at home mean fewer migrants?,” Christian Science Monitor 2018. `https: //www.csmonitor.com/World/Africa/2018/1126/EU-tests-idea-in-GambiaWould-more-opportunity-at-home-mean-fewer-migrants` , [Online; accessed 20-August-2019].

- **Brudevold-Newman, Andrew, Maddalena Honorati, Pamela Jakiela, and Owen Ozier** , “A firm of one’s own,” _World Bank Policy Research Working Paper_ , 2017, (no. 7977).

- **Clemens, Michael A, Claudio E Montenegro, and Lant Pritchett** , “The place premium: Bounding the price equivalent of migration barriers,” _Review of Economics and Statistics_ , 2019, _101_ (2), 201–213.

- **Dao, Thu Hien, Fr ́ed ́eric Docquier, Chris Parsons, and Giovanni Peri** , “Migration and development: Dissecting the anatomy of the mobility transition,” _Journal of Development Economics_ , 2018, _132_ , 88–101.

20
**Cost to the Unbanked for payment services– estimates refer to median income household**

USA 2.5% - 4% Mexico 5% - 15 % Plus- lost interest on savings, lost time, cost of cashing checks and receiving payments all of which adds up to less than optimal choices for savings, investments and borrowing Why don’t the unbanked use banks?

As to why the unbanked do not use banks, in both countries the issue of trust appears to a minor degree, but while the unbanked in Mexico perceive barriers in the costs and requirements of financial institutions, in the U.S. the unbanked point to their own financial situation and lack of savings as a reason for not using banks. In both countries the unbanked pay a significant cost in terms of additional transaction fees, time, and insecurity, in not using formal sector financial services. Concerning initiatives to improve access to financial services, the U.S. has a longer history of both regulatory and private sector practices encouraging retail banking practices. The Mexican government has recently begun to address the situation by focusing on the “popular” banking sector, and assisting credit unions and cooperatives to develop broader services.

Here a serious difference emerges. While a similarly serious percentage of each group (close to one-fifth) underline their discomfort with or lack of trust in banks, the unbanked in the United States do not consider that they have enough money to justify opening an account, whereas the unbanked in Mexico City point to the high costs of opening an account in Mexico City. Actually further analysis from CONDUSEF showed that the main commercial bank savings programs in 2003 were not affordable to 75% of the population, based on an initial and minimum deposit equal half of monthly earnings and maintenance fees of 1% of monthly earnings.

|**Reasons For Not Using Banks**|**USA**|**Mexico**|
|---|---|---|
|Don’t need account – no savings|53%|7%|
|Fees and minimum balance too high|45%|70%|
|Want to keep records private|22%|2%|
|Not comfortable with banks/don’t trust|18%|16%|
|Inconvenience – location and hours|10%|2%|
|Banks won’t let us/lack of documentation|10%|3%|

And, finally, what private-sector and government initiatives are underway to improve financial services for the unbanked?

In both countries governments and the banking industry have developed strategies to help the unbanked gain access to financial services. Both countries have developed programs to support microfinance institutions through grants (USA and Mexico) and subsidized credit programs. The US supports community development institutions, while Mexico’s focus is on lenders to small businesses, car retail and new home developers. In addition, the Mexican government has long managed a postal savings program and is currently developing a major regulatory capability to stimulate growth and modernization in the “social financial sector” (cooperatives and credit 10
Several data sources based on representative surveys of rural households offer the most straightforward descriptive statistics on the effects of employment shocks on migrant workers.<sup>6</sup> Indeed, rural-to-urban migrants in China rarely move to urban areas with their entire families, and thus members of the family (older parents, children, and sometimes spouses) are left behind in home villages. Since 2003, the two survey institutes conducting national rural household surveys—the National Bureau of Statistics (NBS) and the Ministry of Agriculture’s Research Center for the Rural Economy (RCRE)—have been fielding household surveys that included modules with detailed questions on the activities of migrant family members. Much of the empirically based Chinese-language literature detailing the effects of the crisis on migrants is based on these data sources. Unfortunately, these data are not readily available for public use.

Several papers written using the NBS or RCRE household surveys document the gross effect of the crisis on unemployment. Early in 2009, analysts using the NBS survey network estimated that 20 million migrant workers were laid off as a result of the crisis (Chen 2010) and, in March 2009, the NBS released a report estimating that 23 million migrant workers were out of work (NBS 2009).That number amounts to 16 percent of the long-term migrant workforce.<sup>7</sup> Much analysis to date on the effects of the crisis on migrant workers focuses on job loss and does not examine the employment impact based on a reasonable counterfactual assumption of what employment would have been in the absence of the financial crisis. In addition, some of these studies focus on ―gross impacts‖ and thus miss the reallocation of labor across sectors.<sup>8</sup> A study by Huang et al. (2011) uses a panel survey, known as the China National Rural Survey (CNRS), collected by the authors to establish a counterfactual ―business-as-usual‖ level of off-farm employment and then analyze the effect of the crisis on off-farm nonagricultural employment. Because the off-farm nonagricultural employment category treats migrants and non-migrant nonagricultural workers as employed in an integrated off-farm labor market, the net effects of the crisis relative to counterfactual levels of both off-farm employment and overall employment can be examined, including work in agriculture.

10
2 be shared between investors and consumers rather being fully captured by the new owners of the sectors.

The Chilean and Argentinean reforms are the main focus of the paper. It provides an overview and assessment of the impact of what these changes have actually achieved in Argentina and Chile and the main lessons that can be drawn for countries, large or small, with relatively large shares of thermal sources of energy. The paper also shows how the principles underlying these changes can in fact also be adopted and adapted to countries such as Brazil where the large proportion of hydro-based electricity lead many sector specialists to believe that market based instruments would be difficult to implement.

The paper is organized as follows. Section 2 presents how these revolutions allowed the market dreams to turn reality in Chile and Argentina. Section 3 shows how some nightmares still haunt the dreamers. Section 4 explains how Brazil is managing to draw on these experiences in spite of the very particular nature of its generation sources. Section 5 draws the main lessons from the experiences reviewed in the paper.

## **2. How the dreams of light turned reality in Argentina and Chile**

Historically, it is difficult not to describe Chile's experience first. It led the way very clearly in the region and in the developing world in privatization in general. Argentina is also a precursor but more in tenns of the scope and speed with which change took place in infrastructure in particular. It managed to improve not only on what Chile had achieved but many would argue also over what was achieved in the UK. The Chilean and Argentinean privatization experiences in electricity reform are now reviewed in that order.

# _Chile._ **1**

As in most countries considering reform, the conditions that lead to reforms in the sector included price controls, service rationing, overstaffing and large deficits in the public electricity utilities. Within an overall deregulation strategy for the economy but in a context that required strong political support after a difficult earlier privatization wave, the main "declared" goal was increasing the distribution of ownership rather than maximizing revenue or simply returning the economy to the market.

_The privatization strategy._ The overall privatization strategy was simple enough. The utilities had to be transformed in public companies with tradable shares and subject to standard commercial auditing procedures. Privatization per se started in 1986 and most took place within 4 years--only two generation companies were left to be privatized by 1990 and have been privatized since. This was done through three mechanisms:

- the sale of the smallest companies through public auctions --awarding the deal to the highest price bidder--;

- the auction of share packages on the stock market for the largest companies and;

- I This section builds on Bitran, Estache, Guasch and Serra (1997), mimeo
- 21 -

The model was thus weak in explaining the process components underlying implementation (Benson, 1987, p.43). This weakness made utilization of -- evaluations conducted this way difficult. Findings could not be trusted success or failure could not be attributed to the design or content of the program itself, so decisions to accept or reject a program could not firmly be made. Perhaps most importantly, the absence of insight into program implementation left the evaluations of little use to local implementors seeking to improve and refine the program.

The Rand study identified five lessons from research on project implementat,kon with implications for evaluation methodology. First, treatment (or program) effects are indirect. For example, the impact of a new curriculum is mediated by the teachers, students and managers of the school in which it is implemented. Second, implementation choices dominate program outcomes. Third, implementation is a multi-stage, developmental process, in which the nature of the program changes. Fourth, implementors pursue multiple and often conflicting goals. And fifth, decisions made closest to the delivery level for services are most influential in shaping the way in which the program is actually delivered.

These findings applied broadly to a wide range of social change programs, including education change efforts. Other analysts pointed toward particular problems in using the input/output paradigm to evaluate educational programs. Much decision-making on the educational process must contend with the lack of a firm foundation of educational theory. It is difficult for educators to isolate the particular educational intervention that produces a given learning outcome. This inherent quality of schools causes design difficulties for evaluations based on a theoretical mode of the education process (O'Shea, 1981). As one scholar noted: In seeking to evaluate an educational program, we may be led astray if we try to follow too closely the model of the scientific research study. An educational program is not a stable, natural phenomenon with built-in operating characteristics that people may discover and put to use, but not alter in any fundamental way. An educational program is a human artifact, highly complex, infinitely variable, and subject to incessant change. It defies precise definition or accurate measurement. The outcome of a precisely controlled scientific-study of an educational program is almost certain to be either inconclusive or misleading." (Ebel, 1980: p. 228).

In sum, the predominant "input/output" paradigm was weak on two counts, both related to the "black box" of program processes. First, it failed to take into account local contextual and implementation factors, and research showed that these dominated program outcomes. Second, efforts to build theoretical models of the education process to establish the a priori structure of the black box, needed to generate the hypotheses on which measurement rested, were weakened by the absence of good educatiqnal theory. As a result, evaluation findings were difficult to use, either fot program
**<u>Table 4: Poverty Changes and the Sectoral Growth Pattern</u>**

||(1)<br>Benchmark|(2)<br>Controls|(3)<br>Non Upper<br>Middle<br>Income|(4)<br>Non LAC|(5)<br>Positive<br>Growth|(6)<br>Negative<br>Growth|
|---|---|---|---|---|---|---|
|Agriculture (Primary|||||||
|Sector)|-4.875|-3.813|0.892|-4.891|-3.414|-9.645|
||(-1.17)|(-1.00)|(0.46)|(-0.70)|(-0.88)|(-0.71)|
|Secondary Sector|-3.876***|-3.207**|-1.658|-3.493|-2.771**|-5.038|
||(-3.22)|(-2.71)|(-0.96)|(-1.74)|(-2.21)|(-1.11)|
|Tertiary Sector|-2.532**|-3.519***|-1.514|-6.144***|-4.118***|-4.281|
||(-2.61)|(-3.96)|(-1.31)|(-3.49)|(-3.31)|(-0.91)|
|Change in Gini (%)||2.873***|1.803***|2.829***|2.040***|4.747*|
|||(2.91)|(4.19)|(3.01)|(3.54)|(1.92)|
|Constant|0.0706|0.102|-0.0117|0.258|0.111|0.0144|
||(1.04)|(1.50)|(-0.17)|(1.43)|(1.25)|(0.05)|
|Observations|98|98|58|37|68|30|
|AdjustedR-squared|0.094|0.224|0.155|0.246|0.226|0.076|
|*p<.10,**p<.05,**|*p<.01.T-statis|ticsinparenth|eses.||||

Several considerations are worth highlighting. First, there appears to be a difference in behavior between the Latin American and the Caribbean (LAC) Countries and the non LAC countries. Although in the LAC countries agriculture and services appear to be equated with a bad jobs sector, this is not so clear in the non LAC countries. Also, in the non LAC countries the secondary sector is significantly correlated with poverty reduction, while in the non LAC countries this is not so clear, which suggests that services in the LAC countries may be a much more heterogeneous sector, combining good and bad jobs.

Our measures of employment-intensive and productivity-intensive growth are negatively correlated, within sectors. Although this is to be expected because of decreasing marginal productivity and the fact that new entrants into the labor market are younger and so “less productive,” the magnitude of the correlation is particular high for the agricultural sector (see Annex). A possible explanation is that in the short run the measurement of output is less accurate than the measurement of employment—a factor that can be particularly important in the case agriculture and services because of the implicit difficulty in measuring non-marketable output. By including productivity and employment jointly we might be unable to disentangle the sectoral effects. We explore this issue by regressing poverty against our measures of employment and productivity separately (Table 6 and 7).

20
Table A3: Heterogeneous effects of savings default on durable spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|Savings Default|-77.066|-669.228|-135.749|-1094.647|339.052|
||(979.265)|(1238.126)|(935.781)|(1106.193)|(1251.232)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Cash group|6582.93|5866.50|6102.43|6122.34|6158.32|
|R-squared|0.136|0.268|0.210|0.151|0.165|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|Savings Default|-398.757|83.061|-695.900|154.155|-1055.342|
||(1147.975)|(990.830)|(1229.078)|(1107.395)|(980.221)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Cash group|5581.97|6381.43|6321.69|6305.39|6248.18|
|R-squared|0.122|0.088|0.115|0.100|0.082|
|P-value: equal efect of savings default|0.823|0.619|0.708|0.407|0.360|

The top panel includes respondents from female-headed households, those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values are from tests of equality of Savings Default coefficients in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 45