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- <mark>Ferreira, Francisco H. G., Shaohua Chen, Andrew Dabalen, Yuri Dikhanov, Nada Hamadeh, Dean Jolliffe, Ambar Narayan, Espen Beer Prydz, Ana Revenga, Prem Sangraula, Umar Serajuddin and Nobuo</mark>

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they entered primary school since such information is beyond the scope and purpose of these assessments. However, recent work in economics, education, and neuroscience shows that early childhood investments can have large persistent impacts on subsequent education and on later life outcomes (Cunha, Heckman and Schennach 2010, Sylva et al. 2010). For example, results from the OECD’s Programme for International Student Assessment (PISA) show that in countries across the world, participation in quality early childhood education is strongly associated with reading performance at age 15, even after controlling for children’s socioeconomic backgrounds (OECD 2012). As a result, linking early grade assessments with children’s early education experiences can help researchers and policy makers better understand the factors that influence children’s learning during the early years of primary school.

The vast majority of research from developing settings that links early childhood education experiences to children’s primary school performance are evaluation studies. Consequently, they compare children who attended preschools with those who never attended, or compare children who attended preschools with improved quality to those who attended nonimproved preschools (Engle et al. 2011, Nores and Barnett 2010, and the references cited therein). Such comparisons provide evidence for investing in high-quality preschools but they may not adequately capture the reality of many local settings where various types of early education services exist and where children enroll in different types of early education programs at different stages of development. In contexts like these, it is useful to compare different early education service experiences or pathways to understand how they predict children’s transition into primary school and subsequent academic achievement. In one of the few studies that examine early childhood education type and children’s early learning outcomes in a developing setting, Singh (2014) observes that in the State of Andhra Pradesh in India, enrollment in private preschools is associated with significantly higher test scores at the beginning of primary school relative to those in public preschools. The results highlight the role of preschool type in the emergence of test score gaps at school-entry.

Existing research typically does not take into account different types of early education pathways in predicting children’s performance as they transition into primary school given that such data are often unavailable. Our study leverages a uniquely rich data set from rural Indonesia that links children’s test scores in the first few years of primary school with detailed histories on their early education pathways. Moreover, we extend prior research by examining the sequence, 4
This group accounted for two-thirds of the total drop in net job flows in April. A large number of workers in this occupation in Greece are employed in the tourism sector.

We now turn to our more formal analysis of the impact of the pandemic on labor market flows, first using interrupted time series and then difference-in-difference estimates. Regarding the former, we regress the log of total new hires (or separations) on a day-of-week, month, year fixed effects and a linear time trend, and then plot the mean residual by week and year. The results are shown in Figure 7; vertical lines are set at the weeks when the pandemic started and when restrictions on layoffs were implemented. The results confirm what we observed through the descriptive data analysis. There is a pronounced decline in new hires after the pandemic appeared and this decline started in those weeks during which new hires peaked in the pre-pandemic years. At the same time, separations were lower compared to the respective weeks of pre-pandemic “normal” years.

**<u>Table 3. Comparison of net job flows between 2019 and 2020 by occupation</u>**

|<br>|<br>Diffe<br>|<br>rence: Month<br>|<br>2020–Mont<br>|<br>h 2019<br>|
|---|---|---|---|---|
|NACE Rev. 2 sector of economic activity:|January|February|March|April|
|Legislators, senior officials and managers|-10|43|-641|-498|
|Professionals|418|-2890|-3951|990|
|Technicians and associate professionals|1970|-2018|-8894|-1362|
|Clerks|-951|-2508|-11418|-14781|
|Service workers and shop and market sales<br>workers|1333|3456|-35567|-66066|
|Skilled agricultural and fishery workers|43|-89|-417|-388|
|Craft and related trades workers|-156|491|-3285|-1452|
|Plant and machine operators and assemblers|511|-11|-7577|-3452|
|Elementaryoccupations|1860|552|-13527|-16683|
|Total additionaljobs|5018|-2974|-85277|-103692|

Source: ERGANI and National Institute of Labor and Human Resources (NILHR). Authors’ calculations.

As the data show signs of over-dispersion, Equation (1) is also estimated using negative binomial regression. Under this specification, _b1_ indicates the mean daily change on each outcome after the onset of the pandemic and _b2_ denotes the effect of layoff restrictions. Estimation results are summarized in Table 4. The interrupted time series regression estimates confirm the graphical evidence. In 2020, new hires decrease by a significant 1.12%, on average, each day during the pandemic crisis. The effect is more sizeable for part-timers (1.38%) and those in shift work (2.09%), although full-timers are the biggest group in the labor market. Moreover, it seems that the labor market responded with a slight delay, in terms of full-time hires, after the onset of the pandemic, although coefficient estimates are everywhere negative. On the other hand, separations are significantly decreased relative to pre-pandemic days. This is true especially for firings which, as already noted, were restricted during the crisis.

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at the five percent -level, indicating that these effects may be primarily driven by interventions in middle or high income economies.

_Interventions for low-income individuals._ Examining the subsample of 44 studies focusing on lowincome individuals results in a similar picture arising. Effects appear to be higher with increased training intensity and offering financial education at a teachable moment. However, country-level years of schooling and country income are now only marginally significant and insignificant covariates, respectively. Additionally, the coefficient for mandatory courses still has the same sign and similar magnitude, but is estimated with a larger standard error.

_Disaggregating financial behaviors and financial behaviors by target group_ **.** As discussed in section 4.2, it appears to be easier to affect financial behaviors in terms of (retirement-) savings and budgeting compared to borrowing behavior. Thus, we disaggregate the sample into three categories of financial behaviors and search for potentially heterogeneous effects of our main explanatory variables. We reduce the choice of variables for some subsamples to avoid problems with degrees of freedom due to relative few observations.

Column 1 of table 4 shows results for the subsample of 32 studies reporting effect sizes on borrowing behavior. This result matches our main results of the aggregated sample of effect sizes (column 2 of table 3) with significant positive effects from increased intensity, negative effects for low-income target groups, and countries, negative effects from making financial education mandatory and positive effects from offering financial education at a teachable moment. Column 2 of table 4 shows results for the subsample of 20 studies that focus on borrowing as the outcome and have lowincome clients as the target group. Again, results are nearly identical. However, the delay in measurement is now a marginally significant predictor: effect sizes in this sample seem to diminish as time between intervention and measurement of outcomes increases. Hence, treatment effects on debt related behaviors among low-income individuals may be shorter-lived.

Turning to effect sizes reported in 67 studies on (retirement-) saving (column 3 of table 4), we observe that the relevant variables from our benchmark model (column 2 of table 3) remain significant predictors. However, voluntary versus mandatory formats seem to be unrelated to effectiveness. Column 4 of table 4 shows the results on savings and retirement savings for low-income individuals reported in 31 studies. Signs and magnitude are similar to the benchmark estimation, but the only coefficients estimated with a small standard error are intensity per week and the teachable moment. Thus, qualitative results hold, but effect sizes on saving behavior for low-income individuals may be difficult to impact through the considered covariates.

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type II (between 460 and 510 W/m<sup>2</sup> ), type III (between 390 and 460 W/m<sup>2</sup> ), and type IV (lower than 390 W/m<sup>2</sup> ). Each group was offered a fixed tariff: I—RMB 0.51/kWh, II—RMB 0.54/kWh, III—RMB 0.58/kWh, and IV—RMB 0.61/kWh. (See Table 1.)

**Table 1: Category of Wind Energy Resources by Area**

|Category of<br>wind energy<br>resources<br>area|Wind<br>energy<br>density at<br>the height<br>of 70 m<br>(W/m<sup>2</sup>)|Feed-in tariff<br>(yuan/kWh)|Typical areas|
|---|---|---|---|
|Type I|>510|0.51|The areas in Inner Mongolia Autonomous Region,<br>except Chifeng City, Tongliao City, Xing’an League,<br>and Hulunbeier City; Urumuqi City of Xinjiang Uygur<br>Autonomous Region, Yili Kazak Autonomous<br>Prefecture, Changji Hui Ethnic Autonomous Prefecture,<br>Kalamayi City,and Shihezi City.|
|Type II|460~510|0.54|Zhangjiakou City and Chengde City of Hebei Province;<br>Chifeng City, Tongliao City, Xing’an League, and<br>Hulunbeier City of Inner Mongolia Autonomous<br>Region; Zhangye City, Jiayuguan City, and Jiuquan City<br>of Gansu Province.|
|Type III|390~460|0.58|Baicheng City and Songyuan City of Jilin Province; Jixi<br>City, Shuangyashan City, Qitaihe City, Shuihua City,<br>Yichun City, Daxing’anling area of Heleijiang Province;<br>the area of Gansu Province, except Zhangye City,<br>Jiayuguan City and Jiuquan City; the areas in Xinjiang<br>Uygur Autonomous Region, except Urumuqi City of<br>Xinjiang Uygur Autonomous Region, Yili Kazak<br>Autonomous Prefecture, Changji Hui Ethnic<br>Autonomous Prefecture, Kalamayi City, and Shihezi<br>City.;and Ningxia Hui Ethnic Autonomous Region.|
|Type IV|≤390|0.61|Other areas except Category I, Category II, Category III<br>and IV.|

17. To cover the renewable energy (including wind) incremental costs (wind generation cost minus the region’s conventional power network average cost), China’s Renewable Energy Law established a RMB 0.004/kWh<sup>7</sup> electricity surcharge on electricity consumption. Total surcharge revenues that accumulated from 2006 to 2010 amounted to roughly RMB 16 billion (US$2.54 billion), of which 70 percent were used for wind power subsidies.

18. According to the most recent wind power industry development report,<sup>8</sup> gridconnected wind capacity reached 44 GW, and annual wind electricity generation reached 50.1 TWh by the end of 2010. The 12<sup>th</sup> FYP increased the wind capacity target to 100 GW by 2015. The target for 2020 might be increased to 150 GW, 5 times the target announced in 2007 (see Figure 2).

> 7 The surcharge increased to 0.008 RMB/kWh in 2012.

> 8 “Wind Turbine Installed Capacity in 2010,” Chinese Wind Energy Association.

5
# **A Data Appendix**

Section IV gives an overview of the data used in this paper. This data appendix provides more detail on the data sources and construction of the main variables.

# **A1 Administrative Data on Road Construction**

Data on road construction come from the administrative software designed for the management of the program. The data include road sanctioning and completion dates, cost and time overruns, contractor names, and quality monitoring reports.

PMGSY data are posted online ( `http://omms.nic.in` ) at either the habitation or the road level; the data for this paper were all scraped in January 2015. There is a many-to-many correspondence between habitations and roads: roads serve multiple habitations, and habitations may be connected to multiple roads. A census village typically comprises between one and three habitations; approximately 200,000 villages, one third of the total, consist of only a single habitation. For the purposes of this paper, all variables are aggregated to the level of the census village, the geographic unit at which we measure outcomes. We consider a village to be treated by the road program if at least one habitation in the village received a completed road by the year before outcome data were collected.

We matched the administrative road data to economic, population and poverty census data at the village level. In order to generate a village correspondence across multiple datasets, we conducted a fuzzy matching of location names, along with manual cleaning and quality verification.<sup>23</sup> We successfully match over 85% of habitations listed in the PMGSY to their corresponding population census villages.

# **A2 Socioeconomic censuses**

Data on occupation, earnings and assets come from individual- and household-level microdata from a national socioeconomic census. Beginning in 1992, the Government of India has conducted multiple household censuses in order to determine eligibility for various government programs (Alkire and Seth, 2013). In 1992, 1997 and 2002, these were referred to as Below Poverty Line (BPL) censuses. We obtained the anonymized microdata to the 2002 BPL Census from the Ministry of Rural Development. This dataset contains individual demographic variables such as age, gender, and caste group, as well as various measures of household economic activity and assets, which we use to construct baseline control variables.

The fourth such census, the Socioeconomic and Caste Census (SECC), was launched in 2011 but primarily conducted in 2012.<sup>24</sup> To increase the likelihood of collecting data on all

> 23For fuzzy matching, we used a combination of the reclink program in Stata, and a custom fuzzy matching script based on the Levenshtein algorithm but modified for the languages used in India. The fuzzy matching algorithm can be downloaded from the corresponding author’s web site.

> 24It is often referred to as the 2011 SECC, as the initial plan was for the survey to be conducted between

59
_10 PoLish Fiscal Crisis_ Table 3: Profitability and the ;nflation bias All quantities are measured as a percentage of sales at producer prices, except where noted.

|1988|1989|1990|1991|
|---|---|---|---|
|HCA profitability<br>16.9|34.9|23.6|7.1|
|Amortization<br>2.6|1.2|3.4|6.8|
|HCA gross of amortization<br>19.6<br> <br>|36.1<br>|27.0<br>|13.9<br>|
|CF<br>10.5|12.3|13.8|_10.5_|
|ULC<br>19.1|21.1|18.0|22.6|
|IB+IA(m gross HCA - CF)<br>9.0|23.8|13.2|3.5|
|Inventory accumulation||||
|Assumed IL = 1 month<br>-1.2|-8.5|-0.4|-1.9|
|Assumed IL=2 months<br>-1.1|-7.9|-2.1|-2.1|
|Assumed IL = 3 months<br>-1.1|-7.5|-2.7|-2.3|
|Inflation bias<br>||||
|Assumi IL = 1 month<br>10.3|32.3|13.6|5.4|
|Assumed IL = 2 months<br>10.2|31.7|15.3|5.6|
|Assumed IL = 3 months<br>10.1|31.3|15.8|5.7|
|CCA profitability gross of amortization<br> <br>||||
|Assumed IL=1 month<br>9.3|3.8|13.4|8.6|
|Assumed IL=2 months<br>9.4|4.4|11.7|8.3|
|Assumed IL = 3 months<br>9.4|4.8|11.2|8.2|
|CCA profitability||||
|Assumed IL = 1 month<br>6.6|2.6|10.1|1.7|
|Assumed IL = 2 months<br>6.8|3.2|8.4|_1.5_|
|Assumed IL = 3 months<br>6.8|3.6|7.8|1.4|
|Taxation of lB as % of nominal GDP||||
|Assumed IL = I month<br>7.9|10.7|6.4|3.5|
|Assumed IL=2 months<br>7.8|10.5|7.1|3.6|
|Assumed IL = 3 months<br>7.8|10.4|7.4|3.7|

Amortization allowances are set by the Ministry of Finance. The drop in HCA profits caused by the increase in these allowances and the consequent drop in profit tax revenues is therefore in some sense self-inflicted. I will return to this point later in the paper.
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# CASE STUDIES OF TWO IRRIGATION SYSTEMS IN COLOMBIA

# Their Performance and Transfer of Management to Users' Associations

Table 5a Efficiency of Conveyance and Distribution in Coello and RUT Irrigation Districts

||COELLO<br>(Mm<sup>3</sup>)|Efficiency<br>_(O)_|<br>RUT<br>(MiT|Efficiency<br>_(2)_|
|---|---|---|---|---|
|1985|||||
|Semester I|||||
|Total volume diverted|231.9||21.6||
|Total volume delivered|157.9||15.0||
|Efficiency||18.1||69.3|
|Semester II|||||
|Total volume diverted|249.0||20.0||
|Total volume delivered|157.8||12.9||
|Efficiency||63.4||64.3|
|1986|||||
|Semester I|||||
|Total volume diverted|210.8||16.4||
|Total volume delivered|127.5||11.3||
|Efficiency||60.5||68.9|
|Semester II|||||
|Total volume diverted|182.7||16.0||
|Total volume delivered|108.7||9.9||
|Efficiency||59.5||62.3|
|1987|||||
|Semeter I|||||
|Total volume diverted|206.7||19.6||
|Total volume delivered|152.3||14.2||
|Efficiency||73.7||72.3|
|Average Efficiency||65.1||67.6|
a disadvantage relative to males in access to information (Inci et al., 2017) and financial resources (de Andr ́es et al., 2021), which may also affect their decisions to adopt and use technologies in their businesses.

To explore the links between technology and the gender gap in productivity, we leverage a comprehensive database from the Firm-level Adoption of Technology (FAT) survey conducted in 11 countries. In addition to productivity measures and information on top managers’ gender, the FAT database offers a unique opportunity to explore technology adoption at the firm level and overcome a significant gap in the literature. For instance, most studies examining the technological gender gap have focused on gender ownership and a few technologies related to the agriculture sector (Hirpa Tufa et al., 2022; Teklewold et al., 2020; Doss and Morris, 2001) or on individual technologies such as internet connection (Allison et al., 2023) or e-commerce (Lashitew, 2023). In contrast, the FAT survey provides granular information on more than 300 technologies across almost 50 different business functions, allowing us to study the gender lens in various sectors and technologies.

Our results reveal a significant productivity gap between male- and female-managed establishments, with estimates ranging from 24 to 66 percent. Regarding technology adoption for general business functions, our analysis shows that female-managed enterprises are just as likely to adopt the most advanced technologies, except for Enterprise Resource Planning (ERP). In contrast, we find more significant differences in adopting advanced technologies associated with sector-specific business functions. Finally, the results show that technology adoption correlates positively with firm performance and that female management positively affects performance in more technologically sophisticated establishments.

Our analysis contributes to the expanding body of literature investigating the moderating factors influencing the gender gap in productivity. Specifically, the research by Allison et al. (2023) focuses on female ownership and finds that the negative association between female ownership and firm performance can be partially mediated by firms’ access to finance, technology usage, and labor selection. The association between firm size and firm productivity varies between female and male-managed establishments. For instance, female-managed firms’ disadvantage is only significant in small and medium enterprises (SMEs) (Fang et al., 2022). Our findings also align closely to Dezs ̈o and Ross (2012), who suggest that the presence of female executives in senior management positions only deters firm performance in less innovative firms.

The findings of this study have several implications for policy makers. First, our results suggest that policies aimed at reducing the gender gap in technology adoption, particularly for sector-specific business functions, could effectively enhance firm productivity and competitiveness. This could involve targeted initiatives to support female-managed businesses 2
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Spending allocated to address infrastructure needs**

<!-- Start of picture text --> Sub-Saharan Africa<br>low-income countries<br>COMESA<br>middle-income countries<br>fragile low-income countries<br>resource-rich countries<br>Ethiopia<br>Kenya<br>Tanzania<br>Uganda<br>Cape Verde<br>South Africa<br>Congo, Dem. Rep. of<br>0 2 4 6 8 10 12<br>% of GDP<br>capital expenditure operations and maintenance<br><!-- End of picture text --> Source: Foster and Briceño-Garmendia 2009.

**Figure 14. Kenya’s pattern of capital investment in infrastructure differs from that of comparator countries**

Investment in infrastructure sectors as percentage of GDP <!-- Start of picture text --> 3.0 Kenya nonfragil<br>e low-<br>2.5<br>income<br>2.0<br>countrie<br>1.5<br>s<br>1.0<br>0.5<br>0.0<br>public ODA non-OECD PPI<br>% of GDP<br><!-- End of picture text --> Source: Derived from Briceño-Garmendia and others 2009.

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