rafmacalaba/datause-encoder-data huggingface dataset:
To the best of our knowledge, this study is the first to examine the role of management practices as a potential mediator of firm-specific responses to the current pandemic. This is despite a large literature on the role of management practices in steady-state firm performance (see for example (Bloom et al., 2016)), and suggestive evidence from earlier studies of firm responses to crisis (Pal et al., 2014; Aghion et al., 2020). The World Bank Enterprise Survey (WBES) collected panel data on firms before and after the onset of the pandemic in 16 countries, where the pre-COVID-19 panel includes a module on management practices. Although the full data set includes over 8,000 matched observations of firms, the management module in the case of manufacturing sector was restricted to firms with over 20 employees. Arguably, this is reasonable and consistent with the size threshold used in Grover and Torre (2019) and Grover et al. (2019), although it limits the subset of firms in the manufacturing sector. We use the subset of the sample that were administered the management module to examine the role of structured practices as a potential mediator of firms’ pandemic responses. In addition, we examine whether these effects are influenced by specific types of practices related to operational efficiency, incentives, targeting, and monitoring. A unique feature of our data set is that it spans both manufacturing and services firms, allowing us to examine how management’s role varies across these sectors.

We report four main findings. _First_ , structured management practices are associated with more limited downside impacts of crisis on firm performance in manufacturing but not in services. Better managed manufacturing firms, on average, experience a smaller reduction in sales. This effect is most pronounced among firms experiencing an above-median decline in sales. They are also less likely to close temporarily or permanently. Services firms, however, are more exposed to lockdown conditions and demand shocks such that management practices may have less of an opportunity to influence changes in sales and firm closures. _Second_ , in both manufacturing and services, structured management practice scores are correlated with firm’s ability to adjust or convert product mix and shift to online work arrangements. _Third_ , most structured management practices are not associated with a firm’s 2
from Hurricane Strikes in the Central American and Caribbean Regions.” _Journal of Development Economics_ 97 (1): 130–41. https://doi.org/10.1016/J.JDEVECO.2010.12.002.

- Tuholske, Cascade, Zachary Tane, David López-Carr, Dar Roberts and Susan Cassels. 2017. “Thirty Years of Land Use/Cover Change in the Caribbean: Assessing the Relationship between Urbanization and Mangrove Loss in Roatán, Honduras.” _Applied Geography_ 88 (November): 84–93. https://doi.org/10.1016/J.APGEOG.2017.08.018.

- Waite, Richard, Lauretta Burke, Erin Gray and Pieter van Beukering. 2014. _Coastal Capital: Ecosystem Valuation for Decision Making in the Caribbean._ World Resources Institute. https://www.wri.org/research/coastal-capital-ecosystem-valuation-decision-making-caribbean.

- Zhang, Keqi, Huiqing Liu, Yuepeng Li, Hongzhou Xu, Jian Shen, Jamie Rhome and Thomas J. Smith III. 2012. “The Role of Mangroves in Attenuating Storm Surges.” _Estuarine, Coastal, and Shelf Science_ , 11–23. https://doi.org/10.1016/j.ecss.2012.02.021.

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# Educator Knowledge of Early Childhood Development: Evidence from Eastern Nepal

Lindsey Buck<sup>_∗_</sup> Nathan Fiala<sup>_†_</sup> Nishith Prakash<sup>_‡_</sup> Shwetlena Sabarwal<sup>§</sup> Deepak Saraswat<sup>_¶_</sup> Deepika Shrestha<sup>_∥_</sup> _JEL Codes_ : J13, J24, J71, I24 Keywords: Early childhood development, cognitive skills, non-cognitive skills<sup>1</sup>

> _∗_ University of Connecticut, lindsey.buck@uconn.edu

> _†_ University of Connecticut, nathan.fiala@uconn.edu

> _‡_ University of Connecticut, IZA, CReAM, GLO, and HiCN, nishith.prakash@uconn.edu

> §World Bank, ssabarwal@worldbank.org

> _¶_ University of Connecticut, deepak.saraswat@uconn.edu

- _∥_ World Bank, deepika.shrestha11@gmail.com

> 1We would like to thank the Early Learning Partnership, the Inclusive Education Initiative, and the World Bank for their generous funding of this project. Without them, this work would not be possible. We are grateful to World Education, Cristian Aedo, Hanna Alasuutari, Ella Humphry, Ruchi Kulbir Singh, Maya Sherpa, and Mari Shojo for their helpful comments on the paper.
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# _2. Pre-determined circumstances shape lives_

Opportunity sets begin taking form _in utero_ . Who one’s parents are, what country they live in, and how rich they are make a great deal of difference for a person’s opportunities. The opportunity to life itself turns out to depend on such pre-determined circumstances as the education and wealth of parents, whether their house has access to clean water and sanitation, and how close it is to medical treatment. Consider Figure 1, which plots group-specific infant mortality rates across countries. Each vertical line in the figure corresponds to one country, and within each country, the highest point in the line indicates infant mortality (per 1,000 live births) among children whose mothers have no education, while the lowest point gives the corresponding figure for those whose mothers have completed secondary schooling or higher. The differences are striking, not only across countries, but perhaps even more so within them. In El Salvador, for instance, babies born to mothers with no schooling are four times as likely to die before their first birthday as their counterparts with better educated mothers.

**Figure 1  Infant mortality varies across countries but also by mother’s education within countries**

<!-- Start of picture text --> 160<br>140<br>120<br>No education<br>100<br>80<br>60<br>Secondary or higher<br>40<br>20<br>0<br>ColombiaJordanSri LankaVietnamParaguayPhilippinasThailandBotswanaSouth AfricaNicaraguaPeru BrazilGuatemalaTurkeyIndonesiaEgyptTunesiaZimbabweGhanaMoroccoSenegalKenyaEl SalvadprNigeriaTurkmenistanIndiaBoliviaEritreaSudanBangladeshNepalCameroonComorosTogo HaitiUgandaYemenCambodiaCentral African RepublicZambiaPakistanMadagascarBeninBurkina FasoGuineaCote d'IvoireChadMalawiEthiopiaRwandaMozambiqueMaliNiger<br>Infant mortality rate per 1,000 live births<br><!-- End of picture text --> _Source:_ World Development Report 2006, from Demographic Health Survey (DHS) data. _Note:_ The continuous dark line represents the mean infant mortality rate in each country, while the endpoints of the whiskers indicate the infant mortality rates by different levels of mother’s education.

Parental education is not, unfortunately, the only predetermined circumstance that affects the basic opportunity for life. There is some evidence that gender does too, at least in parts of Asia, where juvenile sex ratios are unusually high. A juvenile sex-ratio simply measures the number of 0-4 year-old boys in a population, relative to the number of 0-4 year-old girls. Because slightly more boys than girls are born in a typical population at any given time, that ratio oscillates between 1.00 and 1.05 in most countries. Remarkably, in the Indian states of Punjab and Haryana, the 2001 ratio was above 1.20. In China, it reached 1.17 in 2000. While some recent work suggests that the patterns of incidence of hepatitis B – and the fact that it leads to more male births – may account for
# **Program Evaluation and Spillover Effects**

M. Angelucci<sup>*</sup> and V. Di Maro<sup>+</sup>

# **JEL Classification** : **C93, C81, D62**

**Keywords:** impact evaluation, spillover effects, field experiments, data collection, Indirect Treatment Effect, program mechanisms

_________________________

*University of Michigan and IZA. Correspondence address: University of Michigan, Economics Department, Lorch Hall, 611 Tappan St., Ann Arbor, MI. Email: **<u>mangeluc@umich.edu</u>**<sup>+</sup> Research Group-Development Impact Evaluation (DECIE), The World Bank. Email: vdimaro@worldbalk.org.

 We would like to thank Paul Winters for his extremely useful comments. We also thank Sarah Strickland and Jorge Olave for their help with the editing of an earlier version. The first draft of this paper was prepared as part of the Impact Evaluation Guidelines technical notes (working paper series) of the Office of Strategic Planning and Development Effectiveness at the Inter-American Development Bank. All remaining errors are our own.
and Udry (1999), as cited in Kurukulasuriya and Mendelsohn (2006), reported that in the agricultural development literature, including household labor in net farm revenues is problematic. The net farm revenue is therefore defined without household labor costs and the effect of household labor is controlled for by including household size as an independent variable.

# _5.1.2 Explanatory variables_

# _Climate variables_

Monthly temperature and precipitation data were categorized into four distinct seasons to depict the climatic regimes in Zimbabwe: season 1 – summer, from October to January (ONDJ); season 2 – fall (autumn), from February to April (FMA); season 3 – winter, from May to July (MJJ); and season 4 – spring, August and September (AS). The mean seasonal values of both temperature and precipitation were included in the Ricardian model as well as their quadratic terms. The mean temperatures and precipitation for each season are presented in Table 1.

# _Hydrological variables_

Mean runoff was included in the Ricardian model as the hydrological variable as well as the interaction terms between mean runoff and each climate variable. This was to test the effect of other water sources on farm net revenues and climate sensitivity (Mendelsohn & Dinar 2003).

# _Soil variables_

There is significant variability in soil types across districts and provinces in Zimbabwe. Fiftyfive soil types were available in the data set. The study used correlation analysis between the dependent variables and the soil types and only five soil types were finally included in the final models. The classification of the soils included in the analysis is shown in Table 2.

The main soil types are lithosols, luvisols and chernozems. Lithosols are mainly sandy, rocky and calcareous or siliceous, with salts and gypsum deposits occurring extensively. They are generally very low in inherent fertility and devoid of moisture and vegetation throughout the year. As a result agricultural production on these soils is expected to be low and net returns per unit of land would be limited. Luvisols are usually more fertile and provide a good medium for productive agricultural production for farmers. Chernozems have a relatively high clay content and high organic matter. The texture of these soils is a fairly uniform profile, dark grey to very dark brown to black. They are highly fertile and as such can support crop productivity. Including the soil variable allowed the empirical estimation to capture spatial heterogeneity in net farm revenues across different agro-ecological zones and districts.

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_Box 1: Water Resources Management Institutions and Roles in the Brantas Basin_

|Macro and Program Planning &<br>Budgeting|**:**<br>National, Provincial and District Planning Boards<br>(Bappenas / Prov. Bappeda / Dist. Bappeda)|
|---|---|
|Management/Technical Supervision<br>& Guidance|**:**<br>Ministry of Public Works its agencies (MPW ,<br>DGWR, Directorates)|
|Development|**:**<br>National or Provincial Projects (Proyek Brantas)|
|Regulatory|**:**<br>MPW , MoF, MoSE; Provincial Water Resources<br>Service and Basin Water Resources Management Unit<br>(Dinas PUP, Balai PSDAs); District WR or Public<br>Works Service|
|Water Resources Management<br>Implementation (including major<br>infrastructure O&M for 40 rivers)|**:**<br>Brantas River Basin Corporation (PJT-I) established<br>through Government Regulation No.5 of 1990 later<br>replaced by Government Regulation No.93 of 1999.|
|Irrigation Management|**:**<br>a) District Water Resource Agency (Kab. Dinas PU)<br>for Irrigation Systems within a district<br>b) Provincial Basin Water Resource Agency (Balai<br>PSDA) for irrigation systems that are inter-district.<br>c)ProvincialandDistrictIrrigationCommission|
|Coordination<br>Source: Ramu 2003|**:**<br>a) Ministerial Coordination Team (Tim Kordinasi) at<br>the national level which is expected to be replaced<br>by a National Water Council with stakeholder<br>representation in 2005.<br>b) Provincial Water Resources Committee (PTPA) at<br>the provincial level, which is expected to be<br>replaced by a Provincial Water Council with<br>stakeholder representation in 2005.<br>c) District Water Resources Council (KTPA) to be<br>setup in 2005 with stakeholder representation.<br>d) Basin Water Resources Committee (PPTPA) at the<br>SWS level which is expected to be reorganized<br>with stakeholder representation in 2005.|

# **_5.3 The Brantas Basin Management Corporation (Perum Jasa Tirta I)_**

The Brantas Basin Management Corporation (PJT I) was established by governmental regulation in 1990 as a state corporation to operate and maintain major water resources infrastructure in the basin and to manage its water resources. Due to the basin’s relatively high level of economic development, it can achieve a reasonably high level of O&M cost recovery from water users: hence the logic of corporatizing the WRM function with respect to bulk water supply and allocation.
Table 7 Results of backcasting consumption from 2018/19 NLSS using sectoral GDP growth rates mapped to household head's employment sector

||Poverty rates at the US$1.90<br>poverty line (%)|Number o<br>US$1.90<br>(millions)|f poor at the<br>poverty<br>line|Poverty<br>US$3.20 p|rates<br>at<br>the<br>overty line (%)|Number o<br>US$3.20<br>(millions)|f poor at the<br>poverty<br>line|
|---|---|---|---|---|---|---|---|
||Backcast, pass-<br>through=1<br>HNLSS/NLSS|Backcast,<br>pass-<br>through=1|HNLSS/NLSS|Backcast,<br>pass-<br>through=1|<br>HNLSS/NLSS|Backcast,<br>pass-<br>through=1|<br>HNLSS/NLSS|
|2009<br>2010|46.3<br>56.4<br>42.8|71.4<br>67.8|89.4|76.5<br>73.5|79.5|118.0<br>116.5|125.9|
|2011|41.8|68.1||72.3||117.7||
|2012|40.3|67.3||71.3||119.3||
|2013|38.9|66.9||69.8||119.9||
|2014|37.4|65.9||68.3||120.5||
|2015|36.8|66.6||68.0||123.2||
|2016|38.0|70.6||69.7||129.7||
|2017|38.5|73.6||70.4||134.4||
|2018|39.0<br>|76.3||70.8||138.7||
|2019|39.1<br>39.1|78.5|78.5|71.0|71.0|142.7|142.7|

Note: the table reports the results of the backcasting exercise using a pass-through of 1. The backcasting exercise uses sectoral GDP data from the World Bank MFM-Tool to backcast household consumption from the 2018/19 NLSS survey using information on household head’s sector of employment to map macro- and micro-data. Estimates are reported at the US$1.90 and US$3.20 poverty line and show the poverty headcount rate and the equivalent number of people living below each poverty line. Population data are from the WDI.

33
accessing credit of 10 percentage points on a base probability of around 20%, as well as a significant increase in the amount of credit in levels, estimated at .3 standard deviations. Unfortunately, the purpose of the loan accessed is not reported; however, if the reported magnitude of loans accessed is compared to the reported magnitude of investments in non-agricultural businesses, the median loan accessed is in fact around 30% higher than the median level of non-agricultural investment, suggesting that the increase in credit access is large enough to fund the observed expansion in non-agricultural assets. In Columns (3) through (5), there is evidence of a decrease in staple consumption that is insignificant, and an increase in all other reported consumption as well as total consumption.<sup>24</sup> Non-staple and total consumption increase by .1 standard deviations.

In the appendix, additional results are reported for alternate measures of agricultural investment and income, as well as simple measures of human capital investment. The evidence suggests that the pattern for agricultural investment is quite consistent across different variables employed. There is no evidence of any significant effect on household human capital investment.<sup>25</sup> Taken together, these results suggest that rural households in China that experience – income shocks show a clear pattern of behavior. They disinvest in agriculture i.e., the – income effect for agriculture is negative and increase investment in non-agricultural household businesses. They also consume more non-staple goods, and access new sources of credit. This evidence is broadly consistent with the hypothesis that households are credit constrained at baseline, and the positive income shock relaxes this constraint, allowing households to enter new sectors in which the return to investments may be higher. Further evidence consistent with this pattern will be explored in the next section.

A number of alternate specifications are also explored. First, the climatic index is re-constructed using only geographic information within 50 kilometers of the county cen-

> 24Previous research in Jensen and Miller (2008) presented evidence that rice and wheat are Giffen goods for households in poverty in urban areas in China, suggesting a negative income effect. These results do not indicate a significant negative income effect, but it should be noted that the pattern may be very different for rural vis-a-vis urban households.

> 25These results are discussed in Section S4 of the appendix.

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Economic theory details how problems arising from asymmetric information undermine economic incentives to such an extent that it may be impossible to serve the under-served (Akerlof 1970 and Stiglitz 1974). The major contribution of microfinance has been to demonstrate innovative contracts that, both in practice and in theory, can make commercial lending to the poor viable. Gangopadhyay and Lensink (2009), for example, build on previous work on joint liability borrowing to show how contracts can mitigate adverse selection. Armendáriz and Morduch (2010) show how microfinance contracts reduce moral hazard.

In our 2007 analysis of the MIX data, we investigate the role of contracts empirically (Cull et al. 2007). The study considers lenders that offer different kinds of contracts: conventional bilateral lending agreements, Grameen Bank-style group contracts, and “village banks” that also use group-based methods. Using one observation per institution from 1999 to 2002, we focus on qualitative information on institutions’ lending style, range of services offered, profit status, ownership structure, and source of funds. We include a “lending type” variable that categorizes institutions, separating individual-based lenders, group-based lenders, and village banks. For our profitability regressions, the primary dependent variable is the financial self-sufficiency ratio (FSS), a ratio of revenues and expenses adjusted to account for subsidies. We also regress two other measures of profitability, the operational self-sufficiency ratio (OSS) and return on assets (ROA). A second set of regressions uses “portfolio at risk” as the dependent variable, and a third investigates mission drift by regressing three outreach variables. In this third set, the dependent variables are average loan size over GNP, average loan size relative to the average per capita income of the bottom 20% for the country, and the share of borrowers that are women. While some observers define mission drift narrowly, in terms of the economic level of customers, here we are also concerned with shifts away from an orientation toward female customers. We control for institutional characteristics, including the institution’s age, size, formal profit status, real gross portfolio yield (an approximation of the average interest rate charged by the institution), average loan size (only in the first set of profitability regressions) and region of operation. We also include four financial ratios: capital and labor costs relative to assets, loans to assets, and donations to loan portfolio.

The most telling sign of trade-offs emerges when we investigate how loan repayment rates vary with the interest rates that institutions charge borrowers. The patterns in the data 6
Rajan, Raghuram, G. 2006. “India: The Past and Its Future.” _Asian Development Review_ 23 (2): 36-52.

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Rodrik, Dani, 2007. “The Real Exchange Rate and Economic Growth: Theory and Evidence.” Manuscript, Kennedy School of Government, Harvard University.

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Rodrik, Dani, 2009. “Growth after Crisis”.

~ <u>http://ksghome.harvard.edu/ drodrik/papers.html</u>

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World Bank, 2009d, Service Revolution in South Asia, Washington DC, World Bank.

> wb351921

> M:\Jennifer\Ejaz Items\WILL GLOBALIZATION HELP ECONOMIC RECOVERY IN SOUTH ASIA.FINAL_PAPER.docx

> 9/21/2009 12:31:00 PM

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