rafmacalaba/datause-encoder-data huggingface dataset:
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 14. Performance of the electricity sector in southern African countries**

|**Country**|Botswana<br>(2006)|Lesotho<br>(2009)|Madagascar<br>(2009)|Malawi (2006)|Mauritius<br>(2009)|**Mozambique**<br>**(2007)**|Namibia<br>(2006)|South Africa<br>(2007)|Zambia (2007)|Average|
|---|---|---|---|---|---|---|---|---|---|---|
|Number of power outages in a typical<br>month|1.7|7.2|13.7|6.4|3.6|3.1|1.7|2.2|4.2|4.9|
|Average duration outages(hours)|2.5|5.5|2.3|2.3|3.2|4.3|2.7|4.5|2.9|3.4|
|Lost due to outages(%of sales)|1.4|6.7|7.7|22.6|2.2|2.4|0.7|1.6|3.7|5.4|
|Percentage of firms owning or sharing<br>generator|16|31|29|49|24|13|13|18|14|23|
|Percentage of electricityfromgenerator|18|..|19|3|3|11|6|11|19|11.3|
|Delay in obtaining an electrical<br>connection(days)|25|14|92|98|19|13|9|16|97|42.6|
|Percentage of firms identifying<br>electricityas a major constraint|7|44|55|60|43|25|6|21|12|30.3|

Source: Enterprise Survey database (www.enterprisesurveys.org). _Note:_ Year of the survey is in parentheses.

The comparatively high quality of the power supply reflects the relatively good performance of Electricidade de Moçambique (EDM), the publicly owned electricity utility of for Mozambique. EDM’s collection ratio, at 100 percent of billings, is above the average of other LICs (93 percent) and at the level of other African MICs. The recovery of operational and capital costs increased from 71 percent in 2003 to almost 86 percent in 2006, close to the level of other LICs. Improvements in cost-recovery ratios led to lower hidden costs; for 2005, 2006 and 2008—when the average effective tariff covered more than 80 percent of the total costs—the share of underpricing in total hidden costs was the lowest (figure 13a). Over time system losses deteriorated from 25 percent in 2005 to 27 percent in 2009, above the international benchmark of 10 percent for a well-run energy utility.

**Table 15. Evolution of hidden costs associated with EDM**

||Volume of<br>electricity<br>produced /<br>purchased<br>(GWh/year)|System<br>losses<br>(%)|Collection<br>ratio<br>(%)|Average total<br>cost<br>($/kWh)|Average<br>effective tariff<br>($/kWh)|Total hidden<br>costs<br>($ million<br>/year)|Total hidden<br>costs<br>(% revenues)|
|---|---|---|---|---|---|---|---|
|2005|173|25|100|0.09|0.07|41|41|
|2006|224|26|98|0.10|0.08|44|44|
|2007|216|26|100|0.10|0.07|66|57|
|2008|341|26|100|0.11|0.09|55|37|
|2009|375|27|100|0.11|0.08|84|44|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GWh = gigawatt-hour.

Even putting together underpricing, distributional losses, and collection inefficiencies, EDM turns out to have one of the lowest hidden costs among southern African countries (figure 13b). Hidden costs represent about 44 percent of the EDM’s revenues, almost half of those in Zambia and Botswana, and one-fourth of those in Malawi.

32
government is not operative, the district commissioner becomes the sole discharged of legislative, judicial and executive functions at the district level. No wonder, one sees that local governments are not allowed to succeed in Pakistan (see Shah, 1996)4.

# _Institutional Factors_

Institutional factors also impede effective decentralization. Traditional institutions and mechanism of governance and accountability over time have withered away but these have not been replaced by newer institutions. Instead, all pervasive role of the state have retarded critical look at public policies and institutions. There is almost complete monopoly of the government on institutions of critical thought and media in many countries. Any critical review of government policies and programs invites a government backlash. In Pakistan, rural self government worked well in earlier days of its independence. This system was abandoned in favor of a more centralized system which has resulted in denial of access to basic services by rural population. While lack of institutional capacity was cited as a reason for disbandment of the participatory system, the newer system left a majority of citizens with no voice and participation and no access to basic public services. Indonesia, on the other hand, is now nurturing self-government in rural areas through its village development and poor villages grant programs (see Shah, 1996).

Another important aspect of institutional factors is the citizens' philosophy towards government. Do people generally (politicians, bureaucrats, public employees generally and the public/voter) view the public sector as one where one does service for fair compensation or a position to exploit for personal gain. Various opinion surveys suggest that the prevailing public perception about the public sector especially in South Asia and Africa tend to support the latter view and citizens tend to associate public sector with a predatory role.

Contrary to common misconceptions (see for example, Tendler's characterization of Decentralization and Participation paradigm (D&P)), the success of decentralization policies critically require a strong responsive and accountable government at the nationial level just as the success of centralized governance critically depends upon responsive, accountable and competent field offices of the central government. This is the least understood "paradox of decentralization 4Local elections were held in 1998.

12
consumption expenditures would be statistically significant at 95% confidence, and analogously a 0.25 standard deviation effect on the CES-D depression scale would be statistically significant.

Unexpectedly, the one large and statistically significant result suggests that the program led to worse outcomes: the housing subsidy program leads to a decrease in the socio-emotional wellbeing of children (measured using the SDQ scale) of 0.34 standard deviation units, with a multiple testing adjusted q-value of 0.07.

Beyond the primary pre-specified outcomes, all other outcomes with statistically significant estimated program effects pooled across rounds that survive a multiple hypothesis testing adjustment are reported in Table 4, Panel B. One striking finding is the large negative impact on treatment households’ food security. In particular, adult respondents in the treatment group were 18 percentage points more likely to go to sleep hungry in the past week (46% more likely) than those in the control group (FDR q-value _<_ 0.01), and children and other adults in treatment households were 15 and 13 percentage points more likely to go hungry in the past week (q-values 0.03 and 0.01), respectively. This may be surprising since we did not document a significant reduction in household food expenditures. However, the midline results discussed below highlight important changes in food aid received, household structure, and COVID-19 incidence that appear to have contributed to this finding. The hunger results are not fully robust to using Lee bounds to account for non-random attrition (Panel B, Table A.1), so we interpret these findings as suggestive.

Yet at the same time – and perhaps due to the fact that the program reduced housing expenditures – the program had positive impacts on household savings: pooled over the endline and follow-up survey rounds, treatment households were 8 percentage points more likely to have 30 JDs (roughly 95 USD PPP) in savings, on a base of just 10 percent in the control group (and the q-value of the difference is 0.05, although the finding is less robust to Lee bounds). Though the magnitude of savings is not large, the evidence that households both increased savings and reported more hunger suggests that households may have valued future savings above and beyond current consumption, perhaps as a precaution against anticipated future negative shocks. There is 22
# **A.2 Robustness Tables**

**Table A1: Can Human Experts Predict Sales After 3 Years?**

|||Non-Winner<br>|s<br>||Wi<br>|nners<br>||
|---|---|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|(7)<br>|
||Judges<br>|McKenzie<br>|Fafchamps<br>|Judges<br>|Judges<br>|McKenzie<br>|Fafchamps<br>|
|Business Plan score|-0.0056<br>|-0.0138<br>|-0.0147<br>|0.0011<br>|-0.0065<br>|-0.0116<br>|-0.0110<br>|
||(0.0196)|(0.0204)|(0.0199)|(0.0127)|(0.0131)|(0.0148)|(0.0144)|
|Female||<br>-2.1786<sup>***</sup>|<br>-1.8214<sup>***</sup>|||<br>-0.7631|<br>-0.6095|
|||(0.5237)<br>|(0.5170)<br>|||(0.4225)|(0.4212)|
|Age||0.1618<sup>***</sup><br>|0.1194<sup>**</sup><br>|||0.0318<br>|0.0254<br>|
|||(0.0441)|(0.0437)|||(0.0370)|(0.0363)|
|Graduate Education||-1.0237||||-0.1670||
|||(0.8563)||||(0.5978)||
|Worked abroad||0.6766<br>||||-0.8457<br>||
|||(0.8431)||||(0.5784)||
|Lottery choice||<br>-0.0111||||<br>-0.3200||
|||(0.3986)||||(0.2905)||
|Wealth||0.0144||||0.0850||
|||(0.0779)||||(0.0444)||
|Grit||-0.3675||||-0.0154||
|||(0.3850)||||(0.2711)||
|Ability||0.7581<sup>**</sup>||||-0.1583||
|||(0.2302)||||(0.1617)||
|Agriculture||-0.3835||||-0.3796||
|||(0.4910)||||(0.3675)||
|IT||-0.6148||||-0.4697||
|||(0.6905)||||(0.4347)||
|Manufacture||0.5216|0.8233|||-0.1377|0.1598|
|||(0.5313)|(0.4768)|||(0.3860)|(0.3632)|
|Retail|||1.0215<br>||||1.7465<sup>**</sup><br>|
||||(1.0625)<br><sup>**</sup>||||(0.5318)<br>|
|Self-confidence|||0.3307||||0.1413|
||||(0.1255)||||(0.0937)|
|Motivation|||<br>0.0799||||<br>-0.1323|
||||(0.1469)||||(0.1002)|
|Happiness|||0.1005||||0.1112|
||||(0.1630)||||(0.1281)|
|Optimism|||0.4658<sup>*</sup>||||-0.1384|
||||(0.1862)||||(0.1348)|
|Ability|||<br>0.5508<sup>*</sup>||||<br>-0.1555|
||||(0.2209)||||(0.1503)|
|Credit|||<br>0.2675||||<br>0.0431|
||||(0.1611)||||(0.1960)|
|Eii|30799<sup>***</sup>|24890<sup>***</sup>|09603|10683<sup>***</sup>|10150<sup>***</sup>|07589<sup>*</sup>|04584|
|xstng|.|.|.|.|.|.|.|
||(0.5051)|(0.5406)|(0.6063)|(0.2874)|(0.2866)|(0.3621)|(0.4279)|
|Log(Total award paid)|||||0.5869<sup>*</sup>|0.5552|0.5839<sup>*</sup>|
||||||(0.2930)|(0.2944)|(0.2955)|
|Regional Indicators|No|Yes|Yes|No|No|Yes|Yes|
|Sample Size|1059|1059|1059|1047|1047|1047|1047|
|<br>P-value|,|,<br>0.000|,<br>0.000|,|,|,<br>0.176|,<br>0.005|
|AdjustedR<sup>2</sup>|0.037|0.098|0.138|0.011|0.020|0.024|0.030|
|Robust Standard Errors in<br>Aendix A1 for variable|parentheses.<br>definitions|*, **, *** de<br>eendent vari|note significance<br>able is inverse h|at the 10, 5,<br>erbolic sine|and 1 perce<br>of sales 3 ea|nt levels respe<br>s after ali|ctively. See<br>n coded as|
|pp .<br>zero for applicants witho<br>Fafchamps and Woodruff|.<br>ut an operati<br>(2017) (abbre|p<br>ng firm. P-val<br>viated as FaWo|y<br>ue is for testing<br>) are jointly zero|p<br>that set of co<br>.|y<br>ntrols added|ppy<br>by MacKenz|g,<br>ie (2015) or|
Figure 5.2—Percentage of households below poverty threshold at province level, by pair and treatment status Ifugao versus Neuva Vizcaya Antique versus Iloilo <!-- Start of picture text --> 1997 2000 2003 2006 2009 1997 2000 2003 2006 2009<br>year year<br>Ifugao NV Antique Iloilo<br>60 50<br>40<br>40<br>30<br>20<br>20<br>0 10<br><!-- End of picture text --> Leyte versus West Samar

# Negros Oriental versus Cebu

<!-- Start of picture text --> 1997 2000 2003 2006 2009 1997 2000 2003 2006 2009<br>year year<br>Leyte WS poverty_pct poverty_pct<br>45<br>40<br>40<br>35<br>35<br>poverty_pct<br>30<br>30<br>25 25<br><!-- End of picture text --> Source: Philippine National Statistical Coordination Board, www.nscb.gov.ph/poverty/2009/tables.asp.

32
Table D2: Firm distribution and frequency China firm‐level data by year

|**Year**|**Freq. **|**Percent**|**Cum.**|
|---|---|---|---|
|2009|4,237|9.09|9.09|
|2010|4,237|9.09|18.18|
|2011|4,237|9.09|27.27|
|2012|4,237|9.09|36.36|
|2013|4,237|9.09|45.45|
|2014|4,237|9.09|54.55|
|2015|4,237|9.09|63.64|
|2016|4,237|9.09|72.73|
|2017|4,237|9.09|81.82|
|2018|4,237|9.09|90.91|
|2019|4,237|9.09|100|
|Total|46,607|100||

Table D3: Summary statistics for the variable used for China (2009‐2019)

|**Variable**|**Observations**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|Index * (D/T)|46,607|0.07|0.03|0.02|0.12|
|R&D (0‐1)|46,607|0.55|0.50|0.00|1.00|
|Intg. (0‐1)|46,607|0.79|0.41|0.00|1.00|
|R&D|25,708|15.45|1.44|2.96|21.53|
|Intg.|36,592|16.20|1.66|4.97|23.14|
|R&D per empl.|24,560|8.05|1.18|‐2.45|13.89|
|Intg. Per empl.|34,686|8.83|1.21|‐4.94|16.90|

40
Table 12: Robustness Checks for Poverty Regressions

|Model|SEDLAC<br>weights<br>(1)<br>b/se|$1.25/day<br>poverty line<br>(2)<br>b/se|$4.00/day<br>poverty line<br>(3)<br>b/se|Full-time<br>(4)<br>b/se|Satisfed<br>with job<br>(5)<br>b/se|
|---|---|---|---|---|---|
|Ins|0.620***|0.713***|0.412***|0.706***|0.687***|
||(0.106)|(0.128)|(0.119)|(0.130)|(0.126)|
|Ret|0.801***|1.041***|0.445***|1.012***|1.032***|
||(0.128)|(0.152)|(0.126)|(0.146)|(0.146)|
|Ten|0.091<br>(0.056)|0.012<br>(0.042)|0.110***<br>(0.025)||0.003<br>(0.044)|
|Cont|0.554***|0.545***|0.205|0.509***|0.535***|
||(0.108)|(0.182)|(0.183)|(0.177)|(0.181)|
|Perm|0.164*|0.719***|0.203|0.667***|0.706***|
||(0.099)|(0.095)|(0.133)|(0.098)|(0.097)|
|Sing|-0.075***|-0.214***|-0.089**|-0.282***||
||(0.021)|(0.062)|(0.044)|(0.060)||
|Ftim||||0.302***<br>(0.046)||
|Sat|||||0.324***<br>(0.092)|
|Pseudo-_R_<sup>2</sup>|0.243|0.247|0.225|0.249|0.248|
|Number|6,375,646|6,375,804|6,375,826|6,375,804|6,375,804|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors in parentheses. Significance: *** 1%, ** 5%, * 10%.

Foster methodology, this paper has offered a benchmark analysis of quality of jobs across Latin American and over time (1995-2012) by taking advantage of harmonized – microdata for 17 countries. In addition to labor income approximated with labor earnings that are high enough to keep a worker above the poverty line – the measure considers six dimensions that denote whether the job provides benefits, security, satisfaction, and opportunities to build human capital. Overall, we find that job quality has been growing in Latin America since 2004. This is primarily due to increasing wages. To better understand what factors may be influencing quality we investigate some of the correlates of job quality. The results suggest that the best predictors of job quality are age, gender, education, and whether the job is formal. We also examine the relationship between job quality and poverty, and we conclude 39
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Policy ReseaRch WoRking PaPeR 4884

# **Abstract**

Although there has been considerable recent interest in micro-credit programs, rigorous evidence on the impacts of forming self-help groups to mobilize savings and foster social empowerment at the local level is virtually nonexistent, despite a large number of programs following this pattern. The authors use a large household survey to assess the economic and social impacts of the formation of self-help groups in India. They find positive impacts on empowerment and nutritional intake in program areas overall and heterogeneity of impacts between members of pre-existing and newly formed groups, as well as non-participants. Female social and economic empowerment in program areas increased irrespective of participation status, suggesting positive externalities. Nutritional benefit was more pronounced for new participants than for members of pre-existing groups. Evidence of higher consumption—but not income or asset formation—by participants suggests that at the time of the survey, the program’s main economic impact had been through consumption smoothing and diversification of income sources rather than exploitation of new income sources. Evaluation of such programs in ways that allow heterogeneity of program impact can yield highly policyrelevant insights.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to better understand the impacts of decentralized governance and local development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org or yliu3@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1 Introduction**

For many children worldwide, particularly those from low-income family backgrounds, schooling represents the only avenue for escaping the intergenerational transmission of poverty. However, in the developing world, the common occurrence of aggregate income shocks threatens children’s pursuit of schooling, which their parents view as a costly investment. In these countries, evidence shows that aggregate income shocks increase children’s vulnerability to child labor or child marriage (Corno and Voena (2016), Corno et al. (2020))—two cultural practices known to undermine children’s schooling outcomes (Canagarajah and Coulombe, 1999; Field and Ambrus, 2008). It is therefore not surprising that, when in early 2020 the COVID-19 pandemic erupted, concerns immediately arose over possible setbacks in terms of progress achieved on education outcomes (UNICEF, 2021b). The justification behind these concerns is that this outbreak led to the enactment of protracted economy-wide lockdown measures, with implications for household income and food security. However, since school closure was only temporary, and in some environments schoolchildren were allowed to maintain contact with their schools through distance learning, it is unclear whether such concerns were justified.

This paper uses a unique data set from Nigeria to test the hypothesis that COVID-19 school lockdown measures undermine children’s school resilience by reducing the number of those who returned to school after these measures were lifted. The literature on the effect of COVID-19 on children’s learning outcomes is growing but still predominantly focused on developed countries where distance learning resources are more easily accessible during the pandemic. Yet, of the 142 million children predicted to fall into poverty due to COVID-19, nearly two-thirds live in Sub-Saharan Africa and South Asia,<sup>1</sup> where access to distance learning is already severely limited. Indeed, many children from lowincome backgrounds do not have internet access.<sup>2</sup> Moreover, evidence from past covariate income shocks such as droughts shows that Sub-Saharan African countries, where risk-insurance markets are imperfect, experience a breakdown of informal risk-sharing mechanisms (Kazianga and Udry, 2006; Fafchamps and Gubert, 2007), exacerbating the impacts of these shocks. In this context, it is feared that low-income families’ mitiga-

> 1See _UNICEF data hub_ , accessed online at `https://data.unicef.org/covid-19-and-children/`

> 2See _UNICEF data hub_ , accessed online at `https://data.unicef.org/covid-19-and-children/`

2
spending and desirable outcomes may, in practice, be severed when there is no incentive mechanism in the public sector to use available funds for productive purposes. In explaining the negative link between capital spending and per capita growth, Devarajan, Swaroop, and Zou (1996) note that this may reflect a problem in the link between public spending and service delivery. They argue that while public capital stocks in developing countries have been shown to be associated with economic growth, it may be the case that public spending-as measured from budget documents of countries-does not create any productive capital.

Surveying the literature on the link between public spending and outcomes, Pritchett (1996) notes that all of the negative or ambivalent findings on public spending could potentially be a reflection of differences in the efficacy of spending. These differences could arise due to a variety of reasons including corruption and patronage, and need not necessarily be attributed to bad economic policy. In other words, a unit's worth of public spending does not necessarily buy a unit's worth of service. A good example supporting this theory comes from a public expenditure tracking survey done in Uganda, a poor Sub-Saharan African country. In a survey of 250 primary schools in Uganda, Ablo and Reinikka (1998) found that on average these schools received only 13 percent of the budgetary allocation for non-wage expenditures; the remaining amount-en route from the finance ministry to the facilities-either disappeared or was used for purposes unrelated to primary school education. If a researcher were to use such budgetary information on primary school expenditures from government accounts, she may find that there is very little, if any, impact of public primary education spending on education attainment of primary-schoolers. In reality, such a result reveals little about the true worth of a unit of spending on primary education spending. At the same time, it is difficult for the researcher to find out, over a period of time and across countries, what is the "true" amount of spending on public prograrns.

Yet another reason the link between public spending and outcomes could be broken is the displacement of private sector effort by public spending. This argument is eloquently made in Filmer, Hammer, and Pritchett (2000). Commenting on the weak links that several studies have found between public spending on health and health status, the authors argue, ". . .changes in the price or availability of government interventions may induce a private supply response that can mnitigate any actual impact on health outcomes." Thus, if an increase in public spending on health 4