rafmacalaba/datause-encoder-data huggingface dataset:
which overlaps with, but does not encompass, the treatment and control samples for this intervention. MLSS baseline data collection was conducted in 733 of these schools across multiple rounds between May 2016 and December 2018 (Asim and Casley Gera, 2024).<sup>13</sup> All these rounds were conducted prior to the intervention. 659 schools underwent endline during September 2021 - February 2022. This endline data is used for measurement of outcomes of interest. For more details of the MLSS instruments and procedures, see section A.

# 2.9 Treatment Allocation and Sampling

## 2.9.1 Defining the population frame

To select school communities for treatment and control, we begin by drawing schools from the total population of public primary schools in Malawi.<sup>14</sup> We use administrative data, the 2017 Education Management Information System (EMIS) dataset (MoEST, 2017), which includes 5,552 public primary schools within 34 education districts.<sup>15</sup> Due to its pilot nature, the government requested the intervention to be implemented in schools with both lower and upper grades at primary level. Therefore, we exclude ‘junior primary’ schools which include only Grades 1-4. This leaves 5,405 schools remaining across all 34 districts.

## 2.9.2 Randomized allocation to treatment

A total of 1,198 schools were randomly assigned to treatment and 1,198 to control, across two cohorts of schools. In the first cohort, 800 schools were randomly assigned to treatment and 800 to control. In the second cohort, 398 were assigned to treatment and 398 to control. In both cohorts, the treatment group receives the intervention and the control group receives no intervention. There are no significant differences between the treatment and control groups and population (see Appendix B), and **Table 1** demonstrates there are no significant differences in characteristics between the pooled treatment and control groups.<sup>16</sup>

> 13 Baseline could only be completed in 765 of the 924 schools owing to budgetary and logistical constraints. The authors do not find any significant differences for key school-level variables in the reduced 765 school sample using administrative school census data. Results available on request.

> 14 This population excludes private schools, but includes schools owned by religious institutions which are accredited and financed by Government and considered part of the public school system.

> 15 Malawi has 28 local government authorities, including districts and municipalities. Some of these are subdivided into two or three components for the purposes of education management, producing a total of 34 education districts. In this paper, we use ’district’ to refer to these education districts.

> 16 There are similarly no significant differences between the treatment and control groups within either cohort (see Appendix B)

17
group evidenced substantially more activities than the control group, and this difference is sustained through the post‐intervention period.

We use the data to estimate the differences in log number of activities between the treatment and control groups. The results show no differences in activities in the pre‐intervention period and positive and statistically significant higher levels of activities in the treatment clinics in the intervention and post‐intervention I periods (Table 5). Again, we cannot reject that the hypothesis that the effects are different in the intervention and post‐intervention periods implying that the increase in successful outreach activities persisted after the temporary incentives were removed.

# **6.7 PSYCHOLOGICAL BARRIERS**

In the previous subsection we documented tangible costs of adjustment to increase early initiation of prenatal care. An additional potential cost of adjustment is psychological barriers to change. One way to overcome psychological resistance is to make the guideline or task more salient in the minds of the clinic staff.<sup>26</sup> The issue is not one of lack of knowledge or information as initiating care in the first trimester has been in CPGs since the 1970s and has been a long‐standing part of standard medical education. Rather the issue is the importance or priority that staff place on the task.

The temporary incentives might have increased the importance of early initiation of care in the staff’s minds, thereby making it a higher priority for action. The higher the priority of a task, the less likely psychological barriers would stand in the way of adoption. Kahneman (2012, pp 8) states that “...frequently mentioned topics populate the mind...” more than others and “...people tend to assess the relative importance of issues by the ease with which they are retrieved from memory”. As such, salience “...is enhanced by mere mention of an event” (Kahneman 2012, pp 331). If incomplete or non‐adoption of a task is a matter of salience then the observed treatment effects may be explained by the fact that temporary incentives help to overcome this type of psychological barrier to change.

While we do not have information on the salience of early initiation of care during or shortly after the experiment, we explore whether the temporary fee increase made early initiation of care more important in the minds of the clinic staff after the end of the experiment, using an online survey administered to the chief medical officer of each clinic about the absolute and relative importance of

> 26 Taylor and Thompson (1982) define salience as, “...the phenomenon that when one's attention is differentially directed to one portion of the environment rather than to others, the information contained in that portion will receive disproportionate weighting in subsequent judgments”. See Bordalo et al. (2012, 2013) for a more recent discussion of salience and choice theory. See De Mel et al. (2013), and Karlan et al. (2015) for empirical analysis of salience effects through informational reminders.

17
leads to a 0.38 hour decrease in contemporaneous schooling. Cavalieri (2002) uses propensity score matching and finds that child labor is associated with a 10 percent reduction in the probability of being promoted to the next grade.

Papers using an instrumental variables strategy include Ray and Lancaster (2004), Beegle, Dehejia, and Gatti (2005), and Bezerra, Kassouf, and Arends-Kuenning (2007).<sup>1</sup> Each of these papers has strengths and weaknesses. Ray and Lancaster use micro data from seven countries, but their instruments (household measures of income and assets, and water, telephone, and electricity infrastructure) are unlikely to satisfy the exclusion restriction.<sup>2</sup> Beegle, Dehejia, and Gatti (2005) use community rice price as an instrument for child labor in Vietnam.<sup>3</sup> They estimate that child labor reduces the probability of being in school by 30 percent and educational attainment by 6 percent, but are limited to looking at outcomes over a 5-year horizon. Bezerra, Kassouf, and Arends-Kuenning (2007) use city population, state-level schooling, and literacy rates to instrument for child labor in Brazil. They find that working seven hours or more per day results in a 10 percent decrease in test scores relative to students who do not work. However, their instruments are likely to be correlated to city and state unobservables, and as such are unlikely to satisfy the exclusion restriction. Finally, Ravallion and Wodon (2000) use betweenvillage variation induced by a food-for-school program in Bangladesh; they find that the program led to a significant increase in schooling, but only one-eighth to one-quarter of the increase in schooling hours is explained by decreased child labor.

> 1 Krutikova (2006) uses the same data as this paper and focuses on educational attainment.

> 2 For instance, they find that in Belize the initial hour of child labor leads to a reduction in years of schooling by 2.6 years. Note that in some cases they find the marginal impact of child labor to be positive. In particular, for Sri Lanka, the impact is positive for all school outcomes.

> 3 Beegle, Dehejia, and Gatti (2005) also examine health outcomes for children, as do O’Donnell, Rosati and Van Doorslaer (2005) also in Vietnam.

4
**Table 7: Determinants of energy intensity (CRS, Firm Fixed Effects)**

|**Dependent**<br>**variable=ln(energy/output)**|**Pulp and P**<br>**industry**<br>|**aper**<br>|**Cement in**<br>|**dustry**<br>|**Iron and S**<br>**industry**<br>|**teel**<br>|**Aluminu**<br>**industry**<br>|**m**<br><br>|
|---|---|---|---|---|---|---|---|---|
||Coef.|P-value|Coef.|P-<br>Value|Coef.|P-<br>Value|Coef.|P-<br>Value|
|**Constant**|-1.1964|0|-0.1392|0.177|-2.6302|0|-2.174|0|
|**Ln(price of energy/price of**<br>**output)**|-0.0127|0.856|-0.0318|0.14|-0.1791|0.022|0.5892|0|
|**Ln (technology development)**|-0.0112|0.132|-0.0008|0.811|-0.006|0.517|-0.018|0.08|
|**Ln(Price of energy/price of**<br>**output)*Ln(technology**<br>**development)**|0.0014|0.819|0.0004|0.83|-0.004|0.542|-0.06|0|
|**Foreign capital intensity**|-0.0378|0.891|-1.3637|0.182|-0.303|0.875|-3.26|0|
|**Foreign capital**<br>**intensity*Ln(technology**<br>**development)**|-0.0466|0.075|-0.1089|0.22|0.0662|0.785|0.2836|0|
|**Year 2000**|0.003|0.962|0.0145|0.651|-0.0964|0.17|0.012|0.891|
|**Year 2001**|0.0196|0.761|0.0106|0.743|-0.1773|0.016|-0.001|0.99|
|**Year 2002**|0.0057|0.932|0.0063|0.848|-0.2468|0.001|-0.031|0.734|
|**Year 2003**|0.0061|0.927|-0.038|0.249|-0.4074|0|-0.137|0.148|
|**Year 2004**|-0.0426|0.533|-0.0147|0.666|-0.4696|0|-0.215|0.029|
|**R**<sup>**2**</sup>**(obs.)**|0.8435 (29|0)|0.8192 (67|7)|0.9299 (41|8)|0.9036 (|143)|

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# _<u>Figure 8 – Meta‐Estimates for Work and Earnings</u>_

<!-- Start of picture text --> (a) Income & Earnings Estimates (%)<br>400%<br>350%<br>300%<br>250%<br>200%<br>150%<br>100%<br>50%<br>0%<br>‐50%<br>‐100%<br>Effect Mean Effect CI Business Employment Earnings<br>(b) Income & Earnings Impacts (%) and Transfer Sizes<br>1 300<br>0.8<br>250<br>0.6<br>0.4<br>200<br>0.2<br>0 150<br>‐0.2<br>100<br>‐0.4<br>‐0.6<br>50<br>‐0.8<br>‐1 0<br>Baseline Mean Estimate Monthly HH Transfer (2011 PPP USD)<br>Percentage of baseline<br>Monthly HH Trasnfer<br>Baseline Mean and Estimate<br><!-- End of picture text --> 38
A second qualitative indicator (PD Indicator 11) assesses the quality of recipients’ “results-based frameworks.” It considers (1) the quality of the information generated; (2) stakeholder access to information; and (3) the extent to which the information is utilized within a country level monitoring and evaluation system (OECD, 2008a). The highest grade observed in the sample for this “Results” indicator is B (about 14% of observations), and the most frequent grade is C (about 55%).

“Strategy” and “Results” scores are not available for some of the middle-income country observations in the sample. We therefore do not control for them in every test, but merely show that other key findings are robust to their inclusion.

Donor characteristics, such as differing mandates of bilateral and multilateral donors, may also affect use of country systems (Knack, 2013). Here we control for those influences with donor-year fixed effects.

A final control variable is donor aid share, defined as the percentage of total aid (inclusive of aid not to the government sector) to recipient<sup>_j_</sup> accounted for by donor<sup>_i_</sup> . A higher aid share gives a donor a stronger reputational stake in a recipient’s long-run development, strengthening incentives for the donor to build instead to bypass country systems. Higher donor aid shares are thus predicted to increase use of country systems (Knack, 2013).

The sample mean for donor aid share is 7%, with a minimum value of 0.0014% (Switzerland in Egypt in 2010) and a maximum of 83.6% (World Bank in St. Vincent in 2010).<sup>9</sup> We estimate regressions of the general form:

> 9 Larger donors at the global level do not always have higher average aid shares at the recipient level, as some donors concentrate their aid in fewer countries.  Portugal’s average aid share (23.5%) exceeds that of the U.S. (12.4%), although the U.S. provided more than 100 times as much total aid to countries in the sample as Portugal.

12
Another key component of the external sector position is the FDI position. In all countries studied except Kuwait and Bahrain, FDI assets exceeded liabilities during the past two decades (in Kuwait liabilities exceeded assets; Bahrain maintained a balanced position) (figure 6). Saudi Arabia maintained a large gap between direct investment liabilities and assets. The growth in total direct liabilities slowed in Egypt, Jordan, and Morocco during the early 2000s, which were coupled with the early wave of the global financial crises at the time.

**Figure 6 Net foreign direct investment as percent of GDP, by country, 2007 and 2015**

<!-- Start of picture text --> by country<br>2007 2015<br>AREBHRCOMDJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>.5<br>0<br>-.5<br>-1<br>-1.5<br><!-- End of picture text --> _Source_ : Authors, based on Lane and Feretti 2015.

The net portfolio debt positions indicate that oil exporters are net portfolio investors abroad and net debt asset holders (figure 7). Non-oil-exporting countries, especially Egypt, witnessed volatile net portfolio liabilities positions. Morocco was able to maintain a stable portfolio liabilities position over the past eight years, indicating investor trust in the economy.

<!-- Start of picture text --> Figure 7 Portfolio equity and foreign direct investment holdings<br>as percent of GDP, by country, 2007 and 2015 y g<br>by country<br>2007 2015<br>AREBHRCOMDJIDZAEGYIRQJORKWTLBNLBYMARMRTOMNQATSAUSDNSYRTUNWBGYEM<br>3<br>2<br>1<br>0<br>-1<br>-2<br><!-- End of picture text --> _Source_ : Authors, based on Lane and Feretti 2015.

9
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 18. Zimbabwe’s Internet market and southern African peers**

<!-- Start of picture text --> a. Internet service trends, Zimbabwe, 2000–08 b. Internet service trends, southern Africa<br>20 350<br>5.0 12<br>18<br>4.5 300<br>10 16<br>4.0<br>14 250<br>3.5<br>8<br>12<br>200<br>3.0<br>10<br>2.5 6 150<br>8<br>2.0<br>6 100<br>4<br>1.5<br>4<br>50<br>1.0<br>2 2<br>0.5<br>0 0<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per<br>International Internet bandwidth (bits per second per person)<br>person)<br>Internet users<br>Internet users<br>International internet bandwidth<br>International internet bandwidth<br>Madagascar Malawi Angola Zambia Mozambique Lesotho Zimbabwe Botswana South Africa Namibia Mauritius<br><!-- End of picture text -->

## _Source:_ AICD.

Mobile is the best hope for extending connectivity to most Zimbabweans. Telecel reports its network has population coverage of 68 percent (Orascom Telecom 2009), while Econet states ―all urban areas and most rural areas have been covered (Econet Wireless Zimbabwe 2010)‖ The country is thus close to reaching its coverage gap, which has been calculated to be 13 percent (figures 16, 17) (AICD 2009). The coverage gap represents the proportion of the population living in areas that may not be commercially viable and require some level of public investment or subsidy. There is a universal service fund to which operators contribute 2 percent of their revenues, and which could be used to complete mobile coverage of the population. Consistent with that potential, annual private flows to the sector increased in 2008 and 2009, from average commitments of $17 million in 1998–2007 to average commitments of $162 million in 2008–09.<sup>14</sup>

# Financing Zimbabwe’s infrastructure

To meet its most pressing infrastructure needs and catch up with developing countries in other parts of the world, Zimbabwe needs to expand its infrastructure assets in key areas (table 15). The targets outlined in table 15 are purely illustrative, but they represent a level of aspiration that is not unreasonable. Developed in a standardized way across African countries, they allow for cross-country comparisons of the

> 14 http://ppi.worldbank.org.

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lowest in Europe and Central and South America (0.08 toe per thousand 2015 US$) (Figure 3 scatter plot).

# **3. Green/Clean Investments and Firm’s Productivity in General**<sup>**5**</sup>

A large number of existing studies investigate the relationship between investments in cleaner or greener technologies and the cost of production or productivity. In general, productivity measures the efficiency with which firms, organizations, industry, and the economy convert inputs (e.g., labor, capital, and raw materials) into output. Productivity grows when output grows faster than inputs when both inputs and outputs are measured at the same or constant prices. Depending upon the input in consideration, there are many different measures for productivity, such as singlefactor productivity (e.g., labor productivity or capital productivity) or multifactor (e.g., capital and labor or capital-labor-energy-materials) productivity. It can be measured at the firm or industry, or organization or country level (OECD, 2001). Unless specified otherwise, productivity in this study refers to total factor productivity (TFP)<sup>6</sup> . In this section, the literature that presents overall relationships between green/clean energy investment and productivity at the industry level are discussed, followed by the section on various attributes, such as firm size, ownership, and regulations, that can influence the relationship.

# **3.1. Relationship between green/clean investment and productivity**

> 5 We have divided the discussions on clean/green investments and their impacts on production costs in two sections. The studies analyzing the relationship using the firm level data are presented in Section 3. The firms could be from any sector, manufacturing, electricity supply, transportation services. In Section 4, we discuss studies that focus on a particular sector such as a manufacturing sector (e.g., chemicals), electricity generation sector, power sector, building sector and agriculture sector.

> 6 The TFP, also known as multi-factor productivity, is a measure of the output of a firm or industry or economy relative to the size of all its primary factor inputs. It measures the residual growth that cannot be explained by the rate of change in the services of labor, capital and intermediate outputs of firms or industries or economy as whole. It is often interpreted as the contribution to productivity growth made by factors such as new knowledge, technologies, and experience.

9
# **Aggregation of Regions**

**Kenya, Tanzania and Uganda.** Given our interest in Kenya and Tanzania, we, of course, retain Kenya and Tanzania as two separate regions of the model. The East African Customs Union (EACU) is comprised of Kenya, Tanzania, Uganda, Burundi and Rwanda. Uganda and Rwanda are also regions in the model, but Burundi is not a region of GTAP 8.1 Consequently, we retain Uganda and Rwanda as a separate regions of our model, but not Burundi.

**South African Development Community (SADC).** Given the potential merger of the Common Market for East and Southern Africa (COMESA), SADC and the EACU, we include COMESA and SADC in our model and define regions as close as possible to these regions. Some countries are members of both SADC and COMESA. In these cases, we included them in the SADC region of our model. This included Madagascar, Mauritius, Malawi, the Democratic Republic of the Congo, Zambia and Zimbabwe.

**COMESA.** Since we have to choose where to place countries that are in both SADC and COMESA, our COMESA region is comprised of only eleven countries. Egypt and Ethiopia are individual countries in the GTAP 8.1 data set that are not in SADC or EAC, so we include them in COMESA. The remaining nine countries in our COMESA region are in the GTAP 8.1 region called “Rest of East Africa.” With the exception of Somalia and the territory of Mayotte, all of these nine countries or territories are members of COMESA. Although Burundi is also a member of both the EAC and COMESA, and Seychelles is a member of SADC, this region is dominated by COMESA members, so we “Rest of East Africa” into the COMESA region of our model.

**United States, European Union, China and Rest of the World.** Given potential alliances with the European Union or the United States and the growing importance of China in Africa, we retain the USA, China and the 27 country European Union as separate regions of the model. All other countries of the world are aggregated into an aggregate Rest of the World region in our model.

**Table A2.** In table A2 we show the details of our mapping from the regions of the GTAP 8.1 database to the regions of our model for the five regions in Eastern and Southern Africa.  The mapping for the other three regions of our model should be clear from the previous paragraphs.

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**Table 8**

|**Category**|**Dataset**|**Driver**|**Definition**|**Available in**<br>**NNS**<br>**dataset?**|
|---|---|---|---|---|
||NNS Form 3.1|Mother states child should be<br>exclusively breastfed for 6 months||Yes|
|Caregiver<br>knowledge &|NNS Form 3.2|Mother states child should be fed<br>other foods starting at 6 months||Yes|
|<br>beliefs|NNS Form 1.1|Mother’s educational attainment|Mother has high school or<br>tertiary education|Yes|
||NNS Form 3.1|Mother seeks advice for child’s<br>nutrition|Mother seeks advice from others<br>on child feeding timing,<br>consistency, type or amount|Yes|
|Income|FIES|Total household income||Yes|
|Expenditure|FIES|Total household expenditure||Yes|
||NNS Form 1.3|Government cash transfers||Yes|
|Social support|NNS Form 3.1|Assistance with caretaking from<br>social network||Yes|
||NNS Form 1.1|Mother is married||Yes|
|Food production|NNS Form 1.2|Household has ability to produce<br>food|Household member is the owner<br>or administrator of a farm,<br>livestock or fish pen|Yes|
|Food<br>preservation|NNS Form 1.2|Refrigerator|Household has a functional<br>refrigerator|Yes|
|Caregiver<br>behaviors|FIES|Smokes or drinks|A household member smokes or<br>drinks alcohol|Yes|
|Household<br>setting|NNS Form 1.1|Urban or rural location of household||Yes|
|Time use|Not in<br>questionnaire|Workload & time availability for<br>caregiver||No|
|Access to<br>services|Not in<br>questionnaire|Distance to closest health facility||No|

Measures that proxy for the level of social support and time available for caretaking are mixed (Figure 22). Nationally 55 percent of children under-2 have married mothers which implies a relatively high share of children who live with a single parent. Additional assistance for childcare is prevalent. Over 8 in 10 mothers report they get help with childcare duties, with little differences associated with family income, education or the urban/rural context of the household. Children in the poorest 20% of households are about half as likely to have mothers who work or run a business compared to children in the richest 20% of households. Children under-2 in poorer, rural households or whose mothers have little formal education are more likely to receive assistance from the government’s conditional cash transfer (CCT). Over 8 in 10 children have mothers with some high school or tertiary education. Knowledge of breastfeeding “best practices” is mixed. Nearly 1 in 5 children whose mothers have relatively little formal education believe children should be exclusively breastfed for less than 6 months and nationally, nearly 1 in 3 children have mothers who believe exclusive breastfeeding should extend beyond 6 months. Average household income per month in 2015 was PHP 20,047 (USD 440) 40