rafmacalaba/datause-encoder-data huggingface dataset:
While it is straightforward to allow for sector-specific input cost shares using the results in Appendix A.1, it hardly matters. Cost shares of value-added for Indian manufacturing and service sectors based on national accounts are available from Narayan et al. (2012). As seen in column 2, however, they yield virtually identical elasticity results as in the equal shares case. While the labor cost share in manufacturing is, to be sure, much lower than that in agriculture (and in services), so is the capital share. Hence, the ratio of capital to labor shares, which is most relevant to the calculations, is actually quite similar across sectors.

The third column of Table 1 reports wage-price elasticities ignoring income effects on the demand for services. As argued earlier, _ψ_<sup>_SR_</sup> may provide the more realistic wage response over relatively short horizons, such as the five-year period of my empirical analysis. Intuitively, suppressing income effects dampens the rise in the price of services that accompanies higher agricultural prices. As a consequence, less labor needs to be diverted to the service sector, so the rural wage does not have to rise as much. In the event, _ψ_<sup>_SR_</sup> is 92 percent as large, on average, as the baseline _ψ._ In the remainder of Table 1, we see (col. 4) that segmented labor markets, under otherwise identical assumptions, deliver a much smaller wage-price elasticity than the baseline model.<sup>15</sup> The average value of _φ_ across districts is only 0.45, again barely changing when the equal cost shares assumption is relaxed (col. 5). For completeness, column 6 notes that _φ_<sup>_SR_</sup> = 0. To reiterate, if the income effect on the demand for nontradables is inoperative and labor markets are segmented, then non-agricultural wages are completely unresponsive to agricultural price changes.

# **2.4 Testing the theory**

We have just seen that the wage-price elasticity, _W/_<sup>�</sup> _P_<sup>�</sup> _A_ = _ψ_ , is not a single number for all of India, but rather varies by district according to, among other factors, the sectoral labor

> 15Recall that what matters in the calculation of _φ_ is the importance of services relative to _nonagricultural_ manual labor (services + manufacturing). This services share, _βS_<sup>_′_,averages0.67acrossthe464districts</sup> with any nonagricultural manual labor (based on NSS64; see Appendix B.2).

9
Table 4

# HEANS AND STANDARD DEVIATIONS OF VARIABLES BY DEPARTMENT, 1981-82

|Variables|Me|ans and Stan|dard Devi|ations|
|---|---|---|---|---|
|||Mean|S.|D.|
|_RHS Variables:_|||||
|Per Capita GDP (Current Prices)||174.46|148.|65|
|Share of Industry in Dept.GDP||0.37|0.|20|
|Share of Agriculture in Dept.GDP||0.23|0.|14|
|Share of Setvices etc.in Dept.GDP||0.33|0.|12|
|Share of Govt. Services in Dept.GDP||0.07|0.|03|
|Degree of Urbanization||55.66|21.|76|
|Roads per 1000 Square Kilometers||785.06|2258.|45|
|Post Offices per 1000 Sq. Km.||2.82|1.|92|
||Ur|ban|Ru|ral|
||Mean|S.D.|Mean|S.D.|
|_LHSVariables:_|||||
|Female illiteracy rate: All ages|18.67|<br>10.84|46.22|16.76|
|Male illiteracy rate: All ages|5.44|3.12|19.43|9.09|
|Female/Male illiteracy ratio: All|3.54|0.87|2.52|0.51|
|Female illiteracy rate: 15-19 years|3.80|2.24|18.42|11.20|
|Male illiteracy rate: 15-19 years|1.62|0.74|6.96|3.55|
|Female/Male illiteracy ratio: 15-19|2.45|0.83|2.55|0.63|
|Female illiteracy rate: 20-24 years|5.85|3.99|26.19|14.42|
|Male illiteracy rate: 20-24 years|1.62|0.89|7.58|4.47|
|Female/Male illiteracy ratio: 20-24|3.81|1.47|3.93|1.44|
|Female illiteracy rate: 25-29 years|8.75|5.67|34.50|17.13|
|Male illiteracy rate: 15-19 years|1.95|1.09|10.25|5.93|
|Female/Male illiteracy ratio: 25-29|4.96|2.31|3.84|1.42|
|Female illiteracy rate: 30-34 years|13.88|9.04|44.94|18.56|
|Male illiteracy rate: 30-34 years|3.04|1.83|14.40|8.22|
|<br>Female/Male illiteracy ratio: 30-34|5.01|2.30|3.72|1.77|
|Female illiteracy rate: 35-39 years|22.08|12.77|55.89|17.56|
|<br>Male illiteracy rate: 35-39 years|4.52|2.54|19.83|9.93|
|Female/Male illiteracy ratio: 35-39|5.27|2.07|3.23|1.17|
|Female illiteracy rate: 40+ years|37.72|17.76|69.69|17.04|
|Male illiteracy rate: 40+ years|12.00|6.54|34.01|12.95|
|Female/Male illiteracy ratio: 40+|3.34|0.88|2.17|0.44|

_Notes:_ Means are Unweighted averages.

40
Duflo, Esther and Christopher Udry (2004). “IntraHousehold Resource Allocation in Cote d’Ivoire: Social Norms, Separate Accounts and Consumption Choices,” NBER Working Paper No. 10498 Fajnzylber, Pablo, William Maloney, and Gabriel Montes Rojas. 2006. “Does Formality Improve Micro-Firm Performance? Quasi-Experimental Evidence from the Brazilian SIMPLES program.” Mimeo. World Bank.

FINCA (2007), "Frequently Asked Questions", <u>http://www.villagebanking.org/site/c.erKPI2PCIoE/b.2394157/k.8161/Frequently_Asked _Questions.htm</u> [accessed August 15, 2007].

Grasmuck, Sherri and Rosario Espinal (2000) “Market Success or Female Autonomy? Income, Ideology, and Empowerment among Microentrepreneurs in the Dominican Republic,” _Gender and Society_ 12(2): 231-55.

Kevane, Michael and Wydick, Bruce (2001), "Microenterprise Lending to Female Entrepreneurs: Sacrificing Economic growth for Poverty Alleviation?", _World Development_ , Volume 29, Issue 7, July, p. 1225-1236.

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Lusardi, Anna-Maria and Olivia Mitchell (2006) “Financial Literacy and Planning: Implications for Retirement”, _PRC Working Paper Series No. 2006-1_ McClelland, D.C. (1985) _Human Motivation_ . Glenview, IL: Scott, Foreman.

McKenzie, David and Christopher Woodruff (2006) “Do entry costs provide an empirical basis for poverty traps? Evidence from Mexican microenterprises”, _Economic Development and Cultural Change_ 55(1): 3-42.

McKenzie, David and Christopher Woodruff (2008) “Experimental Evidence on Returns to Capital and Access to Finance in Mexico”, _World Bank Economic Review_ , forthcoming.

McPherson, Michael A. (1996) “Growth of Micro and Small Enterprises in Southern Africa,” _Journal of Development Economics_ , 48: 253-77 Mead, Donald C. and Carl Liedhom (1998) “The Dynamics of Micro and Small Enterprises in Developing Countries,” _World Development_ 26(1): 61-74.

Morduch, Jonathan (1999), "The Microfinance Promise", _Journal of Economic Literature_ , 37(4): 1569-1614.

30

-  -
|**Principle**|**Indicators**|**How Measured**|
|---|---|---|
|Harmonization|**Ind 10a:**Joint missions to the field|Number of joint missions/total number of missions<br>(%)|
|Harmonization|**Ind 10b:**Joint country analytic work|Number of joint analytic work /total number of<br>analytic work(%)|

_Source: World Bank (2011, p.4), see Appendix 1_ While the results of the PD survey provide useful data sources to track progress against the commitments in the Paris Declaration, there are some limitations (World Bank, 2011b). The limitations include (i) the fact that the PD survey tracks only selected PD commitments and does not cover many commitments identified as important for aid effectiveness by the subsequent Accra Agenda for Action (AAA) in 2008; and (ii) methodological concerns regarding some indicators. For example, indicator 3 on ̳aid flows aligned to national priorities‘ is actually more of a measure of aid on budget then alignment per se. Moreover, not only does this not measure what was originally intended, but it even oversimplifies the concept on aid on budget. Therefore, while the advantages of the results of the PD survey still hold, the results of the analysis using the results of the PD survey – including the ones in this paper - need to be carefully interpreted.

The results of the 2011 survey are available for 77 partner countries and 33 selected development partners<sup>9</sup> . Table 2 and 3 summarize the profiles of partner countries and development partners.

**Table 2: Profile** **<u>of Partner Countries which Participated in the 2011 PD survey</u>**

||T|otal|o/w IDA<br>(incl.|eligible<br>blend)|F|CS|
|---|---|---|---|---|---|---|
||No.|%|No.|%|No.|%|
|Africa (AFR)|37|48%|32|42%|10|13%|
|EastAsia andPacific|13|17%|10|13%|2|3%|
|Europe and Central Asia|8|10%|6|8%|2|3%|
|Latin America and Caribbean|11|14%|4|5%|1|1%|
|Middle East and North Africa|4|5%|0|0%|1|1%|
|South Asia|4|5%|4|5%|1|1%|
|**Total**|**77**|**100%**|**56**|**73%**|**17**|**22%**|

_Source: OECD (2011), World Bank classification_

**Table** **<u>3: Profile of Selected Development Partners which Participated in the 2011 PD survey</u>**

|**Group**|**Number **|**Country / Organization **|
|---|---|---|
|Bilateral|22|Australia, Austria, Belgium, Canada, Denmark, Finland, France,<br>Germany, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands,<br>New Zealand, Norway<br>Portugal, Spain, Sweden, Switzerland, United Kingdom, United<br>States|
|Multilateral Development Banks<br>(MDBs)|4|African Dev. Bank, Asian Dev. Bank, Inter-American Dev. Bank,<br>WorldBank|
|Other Multilateral Organizations<br>(Other MOs)|4|EU, IFAD, IMF, United Nations|
|Vertical funds|2|GAVI Alliance, Global Fund|
|**Total**|**32 **||

_Islamic Development Bank is not included in the list, as there is no data of the bank in FCS Source: Author_

> 9 The selection of key development partners is based on an ODA threshold of more than US$100 million and/or their intention to publish data (OECD (2011, p.141))

6
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. High international call charges driven both by technology and market power**

|US$|Percent<br>cases|Call within Sub-<br>Saharan Africa|Call to the<br>United States|Internet dial-up|Internet<br>ADSL|
|---|---|---|---|---|---|
|Without submarine cable|67|1.34|0.86|68|283|
|With submarine cable|33|0.57|0.48|47|111|
|<br>Monopoly on international gateway|16|0.70|0.72|37|120|
|<br>Competitive international gateway|16|0.48|0.23|37|98|

_Source:_ Minges and others, 2009

# Financing Zambia’s infrastructure

Zambia needs to implement an ambitious infrastructure investment agenda over the next decade. In order to meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Zambia needs to expand its infrastructure assets in a number of key areas. The targets outlined in table 9 are purely illustrative in nature, but they represent reasonable aspirations. Developed in a way that is standardized across African countries, they allow for cross-country comparisons of the affordability of meeting the targets, which can be modified or delayed as needed to achieve financial balance.

**Table** **<u>9. Illustrative investment targets for infrastructure in Zambia</u>**

||**Economic target**|**Social target**|
|---|---|---|
|ICT|Fiber-optic links to neighboring capitals and submarine cable|Universal access to GSM signal and public<br>broadband facilities|
|Irrigation|Develop 23,000 hectares that are economically viable for<br>irrigation|n.a.|
|Power|Refurbish 1,700 MW of generation and develop 1,700 MW of<br>new generation|Raise electrification to 24%<br>(50% urban and 15% rural)|
|Transport|Achieve regional (national) connectivity with good quality 2-<br>lane (1-lane) paved road|Provide rural road access to 80% highest value<br>agricultural land, and urban road access within<br>500 meters|
|WSS||Achieve MDGs|

_Source:_ Derived from Foster and Briceño-Garmendia 2009 n.a. = not applicable.

Meeting these illustrative infrastructure targets for Zambia would cost $1.6 billion per year over the next decade. Capital expenditure would account for 70 percent of this requirement. The country’s power needs represent the single largest item and are estimated to be $0.6 billion per year to refurbish 1,700 MW of generation capacity and develop a further 1,700 MW of new capacity. The second-largest item is the expenditure of $0.5 billion a year for the WSS sector, needed to meet the MDGs. Both transport and ICT spending needs are somewhat lower (table 10).

23
makers to a certain level. For example, while the studies for India (Banerjee et al., 2007), Israel (Lavy and Schlosser, 2005), and the US (Jacob and Lefgren, 2004) show that remedial private tutoring improves student performance, this result may hold only for the grade groups evaluated. Clearly, more detailed policies would demand more in-depth analysis of specific groups in particular contexts.

Subject to these caveats, however, it appears that tutoring can generally have substantially positive returns as a supplement to formal public-school education. In addition, the programs on which we have direct evidence may provide good starting points for policy makers seeking to design and implement supplementary education programs.

# **6. What Stance Should Policymakers Take toward Tutoring?  Efficiency and Equity Considerations**

Given the evidence reviewed above, what conclusions can we reach about the efficiency and equity of private tutoring? We discuss our perspectives on these issues in this section using the economic framework from section 2. At first, we do this analysis while maintaining our assumptions that the market is perfect and the supply curve for public schooling is perfectly inelastic after a certain point. Later, we discuss how these conclusions change when those assumptions are relaxed.

## **6.1. Efficiency considerations in the standard case**

The micro evidence on private tutoring suggests, once endogeneity caused by selection is properly accounted for, that private tutoring generally improves student academic performance for the average tutored student. More limited evidence suggests that these improvements can be cost-effective. A question for policymakers is whether from a broader social perspective, the availability of private tutoring increases overall welfare. Are the societal gains from private tutoring likely to be greater than its costs?

Unfortunately, there have apparently been no studies on the efficiency of private tutoring at the macro level. However, combining the micro evidence with the analytical framework from Section 2 helps answer this question. An analysis of Figure 1 suggests that availability of private tutoring increases efficiency and welfare (under certain assumptions that we revisit below). For a household whose demand for education is represented by the demand curve D2, if private tutoring is available, the household consumes education in the amount Q*2. This is more than both the amount of public education (Q2) that the household consumes in the absence of private tutoring and the amount of private education (Q0) that the household can afford.<sup>26</sup> Households gain additional consumer surplus of an amount represented by the triangle BCE, while tutors—who are often public-school teachers working an outside tutoring job—gain producer surplus in the amount represented by the triangle ABE.

14
operator, the level of maintenance of the power system, etc. As such, it is difficult to derive a precise estimate for the impact of power disruptions on an economy tailored to specific locations.

Because of this, we use some relatively recent power outages that have been well documented and studied to derive a range of possible impacts on the economy of an urban center due to an undersupply event. Specifically, we use the February 2021 blackout in Texas and the 2019 rotating blackouts in South Africa. While these two events represent fundamentally different types of events, one where rotating blackouts were necessary throughout the year for a total of about 530 hours and one where a discrete event caused disruptions for many days, they both represent events that were brought on by insufficient generating capacity. We use economic damage estimates from these two events to provide a range of cost estimates.

In the case of Texas, the estimated cost of the power system disruption caused by insufficient supply was estimated at around $130 billion (Busby et al. 2021), out of an estimated GDP for Texas for that year of about $2.05 trillion (Economic Analysis 2022). In other words, the event cost about 6% of annual GDP. “Given the fact that the February 2021 event had associated power outages that spanned 17 days, we compute the average loss per day in GDP, which is about 0.35% per day.” In the case of South Africa, the rotating blackouts throughout the year lasted a total duration of about 530 hours (or about 22 days) (Trace 2020) and cost between $3.5 billion and $7 billion (Trace 2020), out of an estimated annual GDP of about $400 billion.<sup>2</sup> Taking a central cost estimate of about $5.25 billion, the cost of the rotating blackout were about 0.06% per day.

These estimates are simplistic but provide a crude range (spanning nearly an order of magnitude) with which to approximate the economic damage associated with blackouts or rotating blackouts due to insufficient generating capacity. We therefore use a low estimate of 0.06% of annual GDP per disruption and a high estimate of 0.35% of annual GDP per disruption.

> 2 https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=ZA

13
2,781 households listed across 28 villages in Jaunpur district <!-- Start of picture text --> 1,337<br><!-- End of picture text --> No eligible married women aged 18-30 Final HH Listing: 1,444 households with married women aged 13-30 <!-- Start of picture text --> 172<br><!-- End of picture text --> Did not approach HHfor logistic / budget reasons <!-- Start of picture text --> 1,272 households approached for baseline<br>survey<br><!-- End of picture text --> <!-- Start of picture text --> 574<br><!-- End of picture text --> Did not meet other eligibility criteria

# 698 households with eligible women

<!-- Start of picture text --> 5<br>692 eligible women who consented to be part of<br>the study<br>2]<br>BASELINE SAMPLE: 671 eligible women<br>from 28 villages in Jaunpur<br><!-- End of picture text --> Did not consent to participate Did not complete survey (e.g., found to be ineligible during survey)
For literacy and numeracy outcomes, we aggregated all levels of literacy and numeracy skills and constructed a normalized score measure. This measure consists of normalizing literacy and numeracy scores using the mean and standard deviation for the control in _baseline_ . This approach allows comparing our results with other studies targeting primary school age children with edtech strategies such as Banerjee et al. (2007), Muralidharan et al. (2019), among others. Our main analysis also reports the proportion of children providing ‘no correct’ answer in letter recognition, a measure of basic literacy foundations commonly used in the education literature.

To understand what competency levels the interventions may have improved, we assessed the target child and sibling using _Early Grade Reading Assessment_ (EGRA, literacy) and Early Grade Mathematics Assessment (EGMA, numeracy) in Hausa. We used each level of competency to construct an aggregate of learning measures. All levels are reported in Table 1.<sup>10</sup>

# **5.1.2 Mechanism measures**

As described in Section 4, the videos aimed to help parents visualize a future that many of them had not considered before for their children. This involved increasing the aspirations of treated parents by expanding the mental set of choices they had for their children if they were educated. To measure how much our interventions changed the way parents perceived the future of their children, we collected data on parental aspirations and expectations for the target child. For aspirations, we asked parents about what they desired their children to become or to do. For instance, we use questions such as _What is the highest level of education you would like your child to complete?_ and _At what age would you like your child to get married?_ . To measure expectations, we also asked parents about the we use chances that certain outcomes would happen in the child’s future. For example, questions such as _how likely do you think it is that your child will finish secondary school?_ and _how likely do you think it is that your child will get married when your child turns 15?_ To measure parental self-efficacy beliefs about themselves and their children, we asked tion for COVID reasons. Using the ‘wisdom of crowds’ aggregate, a measure that is less sensitive to social desirability biases as it is not about the target child in the household, finds that 79.8% (20.2% out-of-school) of the children are expected to go to school in the next available opening of school admissions. Our aggregated measure constructed from the direct question “Is [Child’s name] currently attending formal school?” gives a proportion of children attending school of 80.9% (19.1% out-of-school). This reveals that our own measure of school enrollment (using a measure of the target child currently attending school) generates the same aggregate information as the measure that is less sensitive to experimenter-demand effects and social desirability biases.

> 10EGRA non-word reading assessment was not used in the aggregate measure of literacy skills because of the small number of observations for this assessment.

16
|**Table 2a: Cor**<br>**_(Rob_**|**relation of Vo**<br>**_ust standard e_**<br>(1)<br>% children<br>with below<br>normal<br>|**te Buying with**<br>**_rrors in parent_**<br>(2)<br>% children<br>with below<br>normal<br>|**Village-level**<br>**_heses;_**<sup>***</sup>**_p<0.0_**<br>(3)<br>Number of<br>barangay<br>health<br>|**Measures of H**<br>**_1_**,<sup>**</sup>**_p<0.05,_**<sup>*</sup>**_p<_**<br>(4)<br>Number of<br>barangay<br>health<br>|**ealth Service**<br>**_0.10)_**<br>(5)<br>Number of<br>barangay<br>health<br>|**s**<br>(6)<br>Number of<br>barangay<br>health<br>|
|---|---|---|---|---|---|---|
||weight|weight|workers|workers|projects|projects|
|**_Bivariate specification, with_**|**_out and with d_**<br>|**_istrictfixed eff_**<br>|**_ects:_**<br>||||
|Vote Buying|0.065<sup>~~***~~</sup><br>|0.05<sup>~~*~~</sup><br>|-6.61<sup>~~***~~</sup><br>|-6.40<sup>~~***~~</sup><br>|-1.09<sup>~~**~~</sup><br>|-1.82<sup>~~***~~</sup><br>|
||(0.03)|(0.03)|(1.38)|(1.57)|(0.51)|(0.52)|
|**_Multivariate specification:_**|<br>|<br>|<br>|<br>|<br>|<br>|
|Vote Buying|0.07<sup>~~**~~</sup><br>(0.03)|0.06<sup>~~**~~</sup><br>(0.03)|-4.84<sup>~~***~~</sup><br>(1.77)|-4.69<sup>~~**~~</sup><br>(1.86)|-1.13<sup>~~**~~</sup><br>(0.56)|-1.34<sup>~~**~~</sup><br>(0.62)|
|Mayor’s family reported as<br>major employer or<br>landowner|0.02<br>(0.01)|0.024<sup>~~**~~</sup><br>(0.01)|0.151<br>(0.97)|-0.13<br>(1.05)|0.03<br>(0.32)|0.07<br>(0.35)|
|Indicator variable = 1 if no<br>clan dominates municipal<br>politics|0.01<br>(0.01)|0.004<br>(0.01)|1.22<br>(0.91)|0.68<br>(0.99)|0.01<br>(0.40)|0.06<br>(0.43)|
|Municipal dependency on|0.05|0.08|-13.04<sup>~~*~~</sup>|-11.4<sup>~~*~~</sup>|-0.06|-0.14|
|<br>national revenue share|(0.08)|(0.08)|(7.4)|(6.98)|(2.13)|(2.11)|
|Barangay poverty estimate|0.001<sup>~~*~~</sup><br>(0.0004)|0.0005<br>(0.0004)|0.02<br>(0.03)|-0.0004<br>(0.04)|-0.005<br>(0.01)|-0.0080<br>(0.01)|
|Barangay population (log)|0.001<br>(0.01)|0.01<br>(0.01)|0.51<br>(0.69)|0.78<br>(0.85)|-0.44<br>(0.31)|-0.32<br>(0.31)|
|Barangay location (travel<br>time to_centro_)|-0.0001<br>(0.0001)|-0.0001<br>(0.0001)|0.035<sup>~~***~~</sup><br>(0.013)|0.039<sup>~~***~~</sup><br>(0.013)|0.013<sup>~~***~~</sup><br>(0.005)|0.013<sup>~~***~~</sup><br>(0.005)|
|Barangay roads need<br>repair|-0.001<br>(0.01)|0.008<br>(0.009)|1.06<br>(1.08)|1.54<br>(1.24)|0.16<br>(0.28)|0.12<br>(0.33)|
|Barangay “social capital”<br>(% reporting_bayanihan_)|-0.015<br>(0.033)|0.0057<br>(0.03)|-0.923<br>(2.35)|-0.77<br>(2.7)|-0.83<br>(0.95)|-0.83<br>(0.94)|
|District FE|No|Yes|No|Yes|No|Yes|
|District 1||0.04<sup>~~***~~</sup><br>(0.01)||1.33<br>(1.31)||-0.16<br>(0.52)|
|District 2||0.006<br>(0.02)||2.12<sup>~~*~~</sup><br>(1.31)||-0.13<br>(0.41)|
|District 3||0.009<br>(0.02)||0.24<br>(1.8)||-0.60<br>(0.49)|
||57|57|51|51|58|58|
|N, R-sq|0.282|0.405|0.428|0.478|0.303|0.321|

31