rafmacalaba/datause-encoder-data huggingface dataset:
**Table 4: Proportion of households owning at least one appliance**

|Appliance|Ownership<br>share|Appliance|Ownership<br>share|
|---|---|---|---|
|Air cooler|68.9%|Television|73.5%|
|Air conditioner|7.4%|Refrigerator|60.1%|
|Room heater|1.2%|Water purifier|7.6%|
|Warm air blower|0.3%|Microwave oven|2.9%|
|Ceiling fan|92.8%|Mobile charger|97.2%|
|Table fan|16.3%|Desktop computer|4.7%|
|Immersion rod|4%|Laptop computer|10.7%|
|Water heater (geyser)|10.5%|Electric iron|33.2%|
|CFL|78.1%|Sewing machine|1.5%|
|Bulbs|39.6%|Water pump|37.4%|
|Tube lights|34%|Washing machine|19.2%|

Notes: Proportions are calculated using sampling probabilities

**Table 5: Proportion of households owning at least one appliance**

|Dependent Variable: log(daily energy services demand<br>based on surveydata)|(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|log(observed daily consumption)|0.546***<br>(0.0557)|0.6***<br>(0.0624)|0.603***<br>(0.0636)|0.602***<br>(0.0656)|0.606***<br>(0.0678)|
|Constant|0.225***<br>(0.0723)|0.148*<br>(0.0797)|0.144*<br>(0.0819)|0.145*<br>(0.0854)|0.138<br>(0.0901)|
|Observations|805|780|769|760|749|
|R-squared|0.294|0.293|0.291|0.282|0.277|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Notes: Observed daily consumption is calculated as <u>f e e e p</u> based on the billing data set. The daily energy ) (ssccii gg f ggmmggcciimmiicci e ccggnnsscciiggdd cc e mm hggccssgghggmmdd ggooggmm m mm cciim ss iinn ggnngg eggm pggmmiiggdd ) <u>e e e</u> (nncciiccggmm ggff ddmmeess ccggiibbgservices demand based on survey data is calculated asnn ffiimmssii mmnndd m ssii cciim mmggccggiiooggdd ccee iihgg hggccssgghggmmdd ggooggmm iihgg eeggm 365 ~~.~~ mmn ccm ggnnggm e ssggmmooiiccggss ddggiimmnnddggdd cc e iihgg hggccssgghggmmdd ccmmssggdd ggnn ssccmmoogg e ddmmiimm Model (1) comprises of the entire sample of 805 households, whereas models (2), (3), (4) and (5) correspond to trimmed samples, comprising of households with monthly consumption above 14.9, 16.5, 18.2 and 20.1 units in the billing data respectively.

47
outside the cope of our paper, estimates at Lakdawala lines are available for two additional years: 1983 and 1987-88.

Table 7 reports the poverty estimates with the states arranged in descending order of their populations. Several observations follow. First, taken as a whole, poverty fell in each of the 17 states between 1993-94 and 2009-10. When we disaggregate rural and urban areas within each state, we still find a decline in poverty in all states in each region over this period. Indeed, if we take the ten largest states, which account for three-fourths of India’s population, every state except Madhya Pradesh experienced a decline in both rural and urban poverty between every two successive surveys. The reduction in poverty with rising incomes is a steady and nationwide phenomenon and not driven by the gains made in a few specific states or just rural or just urban areas of a given state.

Second, acceleration in percentage points per year poverty reduction during the highest growth period of 2004-05 to 2009-10 over that during 1993-94 to 2004-05 can be observed in 13 out of the 17 states. The exceptions are Uttar Pradesh and Bihar among large states and Assam and Haryana among medium-size states. Of these, Uttar Pradesh and Assam had experienced at best modest acceleration in the Gross State Domestic Product (GSDP) during the second period while Haryana had already achieved a relatively low level of poverty by 2004-05. The most surprising is the negligible decline in poverty in Bihar between 2004-05 and 2009-10 since its GSDP had grown at a doubledigit rate during this period.

**Table 7** : Rural and urban poverty in Indian states (percent)

|State|1993-<br>94|Rural<br>2004-<br>05|2009-<br>10|1993-<br>94|Urban<br>2004-<br>05|2009-<br>10|1993-<br>94|Total<br>2004-<br>05|2009-<br>10|
|---|---|---|---|---|---|---|---|---|---|
|Uttar Pradesh|50.9|42.7|39.4|38.2|34.1|31.7|48.4|41.0|37.9|
|Maharashtra|59.2|47.8|29.5|30.2|25.6|18.3|48.4|38.9|24.8|
|Bihar|62.3|55.7|55.2|44.6|43.7|39.4|60.6|54.6|53.6|
|Andhra Pradesh|48.0|32.3|22.7|35.1|23.4|17.7|44.7|30.0|21.3|
|West Bengal|42.4|38.3|28.8|31.2|24.4|21.9|39.8|34.9|27.1|
|Tamil Nadu|51.0|37.6|21.2|33.5|19.8|12.7|44.8|30.7|17.4|
|Madhya Pradesh|48.8|53.6|42.0|31.7|35.1|22.8|44.4|49.3|37.3|
|Rajasthan|40.7|35.9|26.4|29.9|29.7|19.9|38.2|34.5|24.8|
|Gujarat|43.1|39.1|26.6|28.0|20.1|17.6|38.2|32.5|23.2|
|Karnataka|56.4|37.4|26.2|34.2|25.9|19.5|50.1|33.9|23.8|
|Orissa|63.0|60.7|39.2|34.3|37.6|25.9|59.4|57.5|37.3|
|Kerala|33.8|20.2|12.0|23.7|18.4|12.1|31.4|19.8|12.0|
|Assam|55.0|36.3|39.9|27.7|21.8|25.9|52.2|35.0|38.5|
|Jharkhand|65.7|51.6|41.4|41.8|23.8|31.0|61.1|47.2|39.3|
|Haryana|39.9|24.8|18.6|24.2|22.4|23.0|35.8|24.2|19.9|
|Punjab|20.1|22.1|14.6|27.2|18.7|18.0|22.2|21.0|15.8|
|Chhattisgarh|55.9|55.1|56.1|28.1|28.4|23.6|51.1|51.0|50.3|
|India|50.1|41.9|33.3|31.7|25.8|20.9|45.5|37.9|29.9|

19
(Italy), the electronic business registry reduced the average time for correcting errors (“suspended registrations”) from 10 days to a half day. The benefits of business registry reforms implemented during the last decade have led to web-based overtaking paperbased transactions in developed countries such as Italy (2003) and Denmark (2004). Advanced electronic business registries can also aggregate and analyze data, which can provide an important tool for market surveillance and business monitoring, such as for attracting foreign direct investment.

While the degree of progress in the modernization of business registries varies greatly, countries usually have a common goal to evolve from a paper-based business registry to a one-stop, automated, web-enabled registry capable of delivering products and services online via transactions involving authenticated users and documents. The implementation of such an electronic business registry has a positive impact not only in the ease of creating a business, but also in other aspects of the business cycle (Table 2).

**Table 2: Potential Uses of an Electronic Business Registry**

|**Potential Users of an e-BR**|**Potential use of e-BR services:**|
|---|---|
|A business owner ...|... registers a new business, lists the board of directors<br>of his/her firm, makes changes to the business’s<br>contact details, or files annual financial statements.|
|A loan approval officer at a|... confirms the financial health and history of a|
|financial institution ...|potential borrowing firm and owners.|
|A potential customer ...|... confirms information on the operations and<br>management of the firm.|
|A potential supplier ...|... confirms the financial health of a potential buyer in<br>making trade credit decisions.|
|A lawyer or notary ...|... signs in to validate information.|
|A government official (e.g. a|... verifies a firm’s active business status.|
|tax, customs, pension, VAT or<br>social security authority official)||

Source: Lewin and others (2007) 15
**Figure 1  Estimates of primary student dropouts by 2020 growth projections release**

<!-- Start of picture text --> 0.0 4.5<br>MPO-Mar WEO-Apr MPO-May MPO-Oct<br>4.0<br>-1.0 -0.9 3.8<br>3.5<br>-2.0 3.0<br>2.9<br>-2.4<br>2.5<br>-3.0 2.3<br>-3.4<br>2.0<br>-4.0 1.5<br>1.4<br>1.0<br>-5.0<br>-5.1<br>0.5<br>-6.0 0.0<br>Projection Release Date<br>Growth Projections 2020 (p.p.)* Number of Primary School drop-outs (in millions of students)<br>(in millions of students)<br>Growth Projections 2020 (p.p.)*<br>Number of primary school drop-outs<br><!-- End of picture text --> Note: GDP per capita growth projections in for 2020 from WB-MPO (March, 2020); IMF-WEO (April, 2020); WB-MPO (May, 2020); and WB-MPO (Oct, 2020); (*) Growth projections are weighted by the student cohort of each country, and will different from other global averages reported in the original publications, in are weighted by GDP.

# **Scenarios**

- All scenarios assume that, as of today, remote learning cannot fully match face-to-face delivery. <mark>Although most countries have made heroic efforts to put remote and remedial learning strategies in place, learning losses are accumulating rapidly. A recent survey on national education responses to COVID-19 by UNICEF, UNESCO, and the World Bank shows that while countries and regions have responded in various ways, only half of the initiatives are monitoring usage of remote learning (Figure 2, top panel).  Moreover, where usage is being monitored, the remote learning is being used by less than half of the student population (Figure 2, bottom panel), and most of those cases are online platforms in high- and middle-income countries.</mark>

20
sector. The elasticity of substitution between nonresource traded and nontraded goods,  K, is set to 0.4, to capture a relatively low degree of substitution across production sectors. The rate of depreciation of private capital, <sup>P</sup> , is set at 0.045, a fairly standard value. To capture initially high adjustment costs to private capital, the parameter κ is set at 25. The direct complementarity effect of public capital on private investment,  K, is assumed to be absent initially, which implies that  K = 1. The sensitivity analysis with a positive value (that is, a lower value of  K) will be discussed later.

The relative sizes of the different production sectors are calculated, based on the data published in the annual report of the Central Bank of Congo (2013, Table I.2). The size of the resource sector, which corresponds to p<sup>R</sup> Y<sup>R</sup> /Y in the model, is calculated as the share of extraction industries in total GDP at factor cost—the relevant concept in the model—in 2012, that is, 31.3/95.4 = 32.8 percent. The size of the nonresource tradable sector in 2012, which corresponds to Y<sup>T</sup> /Y in the model, is estimated by adding the nonresource primary sector (namely, agriculture, forests, etc.) to manufacturing industries, that is (9.0 + 7.8)/95.4 = 17.6 percent. Thus, the size of the nontradable sector in 2012, which corresponds to z<sup>-1</sup> Y<sup>N</sup> /Y, in the model, is determined residually and is given by 1 - 0.328 - 0.176 = 49.6 percent.

For the resource sector, and given the previous discussion about the magnitude of DRC’s natural resources, the degree of persistence in production,  yR, is taken to be very high at 0.96, and the standard deviation of the nonsystematic shock <sup>yR</sup> is set at 0.1. For 21
Policy Research Working Paper 9774

# **Abstract**

This study explores how businesses in Tanzania are impacted by floods, and which strategies they use to cope and adapt. These insights are based on firm survey data collected in 2018 using a tailored questionnaire, covering a sample of more than 800 firms. To assess the impact of disasters on businesses, the study considers direct damages and indirect effects through infrastructure systems, supply chains, and workers. While direct on-site damages from flooding can be substantial, they tend to affect a relatively small share of firms. Indirect impacts of floods are more prevalent and sizable. Flood-induced infrastructure disruptions—especially electricity and transport—obstruct the operations of firms even when they are not directly located in flood zones.

The effects of such disruptions are further propagated and multiplied along supply chains. The study estimates that supply chain multipliers are responsible for 30 to 50 percent of all flood-related delivery delays. To cope with these impacts, firms apply a variety of strategies. Firms mitigate supply disruptions by adjusting the size and geographical reach of their supply networks, and by adjusting inventory holdings. By investing in costly backup capacity (such as water tanks and electricity generators), firms mitigate the impact of infrastructure disruptions. The study estimates that only 13 percent of firms receive government support in the aftermath of floods.

This paper is a product of the Global Facility for Disaster Reduction and Recovery; Urban, Disaster Risk Management, Resilience and Land Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jrentschler@worldbank.org, ekim11@worldbank.org, aerman@worldbank.org, sdevriesrobbe@worldbank.org, and stephan.thies@fu-berlin.de.

_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
On technology adoption, our analysis of non-cognitive skills driving cash crop adoption pushes forward the nascent literature bridging behavioral and agricultural economics [e.g. Duflo _et al._ 2010, Liu 2013]. In doing so we also add to the literature emphasizing heterogeneity across individuals in the returns to technology adoption [Suri 2011, Zeitlin 2012]. On family economics, our finding that female non-cognitive skills matter for household adoption of a cash crop, _holding constant_ her husband’s non-cognitive skills, complements the literature refuting the standard model of household decision making that treats the farm household as a single unit [e.g. Udry 1996, Duflo and Udry 2004, Goldstein and Udry 2008].

The remaining of the paper is organized as follows. Section 2 discusses the IHPS data and the methodology to measure non-cognitive skills, together with the characteristics of women farmers, their families, farms and communities. Section 3 presents evidence on the impact of female non-cognitive skills on cash crop adoption, together with robustness checks and potential mechanisms. Section 4 concludes.

# **2. Data**

## **2.1. Sample**

The data used in this paper come from the second wave of the _Malawi Integrated Household Panel Survey_ (IHPS), collected in 2013. We examine the effect of the noncognitive skills of women farm managers on tobacco adoption. To avoid the confounding effect of marital status we focus our attention on married women. This restriction is appropriate because only a very small number of non-married women grow tobacco.

Farm households were asked to identify “who in the household makes decisions concerning crops to be planted, input usage, and timing of cropping activities?” We use the answers to this question to identify women farm managers. It is common for women to be involved in country settings (where such measures have been validated). Moreover, they show that combining the measures according to such pre-existing scales leads to low internal consistency.

5
- 18 -

become less Important than banks. With the Implementatlon of development plans, official lending complements but clearly does not supersede Informal sources.

Sample surveys supplying the Information on the extent of Informal lending practices Indicate that thelr volume Is far greater than that of organized Institutions. Informal lending Is characterized by a much shorter processing ti,r,e, better screening techniques or enforcement devices (noted In the lower default rate), and higher Interest rates, with a medlan neariy twice as high and a variance much higher than that of institutionalized credit rates (see Slngh [1968], Harris (1982], Bottomley (1975] and Desal (1983]).

The lower delinquency rates reported for Informal credit sources are to a large extent due to better assessment of creditworthiness, ability to exert social pressure for repayment, and the frequent practice of tying (interlinking) credit contracts with other Input or output contracts. Documentation of the use and characteristics of the latter practice Is quite extensive. Sharecropping contracts are quite often Interlinked with credit contracts (e.g., BharadwaJ [1974], Bhadarl (1977], Bardhan (1984J, Blnswanger et al. [1984], Bell & Srlnlvasan (1985]). Credit contracts between landlords and tenants are often In the form of production loans and tied to the purchase of fertilizer, seeds, and other forms of capital (see Singh, [1984, Ch. 10]; Braverman & Stigiltz [1986a]) with different tenants pa,ing different Interest rates on their loans (see Bardhan & Rudra [1978]). These Interlinkage practices have been viewed as a way to address the adverse selection problem (Braverman & Guasch (1984]) and the moral hazard problem Indigenous to these markets (e.g., Braverman & Srlnivasan [1981], Braverman & StiglItz (1982], Mitra (1982], and Bell & Zusman [1980]). (For an elaborate overview of this literature, see Braverman & Guasch (1986].)
to 17. Additionally, it included 745 Venezuelan children and 637 Venezuelan adolescents. The survey also gathered information from the parents of these minors, involving data from 3,614 adults, 40% of whom were Venezuelan migrants.

# **III.B Implementation**

The first round of the survey was conducted face-to-face from October to December 2022, while the second round took place during the same months in 2023. This paper analyzes data from the initial wave. Each survey session spanned approximately two hours. To encourage participation, households were compensated with a grocery voucher valued at 30,000 Colombian pesos (about USD $8) for each round. The questionnaire was predominantly completed by the child’s main caregiver, usually the mother, given their central role in the child’s daily life and the decision-making processes affecting their welfare. In instances where the primary caregiver was unavailable, a partner or the head of the household was permitted to respond to questions concerning the adults’ labor market characteristics. The survey team consisted of 35 enumerators, including 12 social workers and 23 psychologists, all of whom possessed significant experience working with vulnerable all enumerators underwent on populations. Additionally, comprehensive training administering child scales and conducting anthropometric measurements.

_Survey modules._ The survey instrument consisted of seven main modules aimed to gather comprehensive information on various aspects of the well-being and development of children and adolescents in the context of forced displacement. This comprehensive questionnaire was divided into seven sections and was administered by enumerator pairs, where one interviewed the main respondent, typically the primary caregiver of the child, while the other engaged directly with the child or children. The choice of the primary caregiver as the main respondent was deliberate, given their extensive knowledge about the child’s daily life and needs. Enumerators identified the principal caregiver by inquiring who fulfilled this role, using a specific definition: ”The primary caregiver is considered the 17
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average annual frequency of significant landslides during the period from 1980 until 2018 from the World Bank; and (v) _pollution_ – annual average levels of PM 2.5 in 2019 from NASA.

For infrastructure, we rely on data from the Harmonized Global Critical Infrastructure index <u>(Nirandjan et al., 2022), capturing access to (i)</u> _energy infrastructure_ , including cable, line, minor line, power tower, power pole, plant, and substation; (ii) _water infrastructure_ , including water towers, water wells, reservoirs, and water works; (iii) _waste management facilities_ for solid and water waste; (iv) _transportation infrastructure_ , including roads, railways, and airports; (v) _education infrastructure_ , including kindergartens, schools, colleges, universities, and libraries; and (vi) _health infrastructure_ , including ambulatory and non-ambulatory medical facilities.

The last line in the top panel of <u>Table 4 reports the</u> **χ**<sup>**2**</sup> -test of joint significance of the administrative risk and resilience variables, based on the regression in which the 11 risk and resilience variables are added to the baseline specification; the last line in the bottom panel reports the marginal effect of age in specifications that include the administrative data on environmental risk and resilience. The risk and resilience variables are jointly significantly correlated with climate beliefs, but they have no significant effect on the magnitude of the marginal effect of age on climate beliefs.

In <u>Table 5,</u> we undertake a similar exercise for the two climate action variables related to government priorities and willingness to pay higher taxes to support the climate agenda. One key difference is that when examining the determinants of climate action, we also include the two dependent variables from <u>Table 4</u> related to perceived risks of climate impacts as explanatory variables. Their inclusion is motivated by the notion that perceived risks of climate change could partially determine climate actions. The estimates bear out this intuition: respondents who believe climate change will affect them personally or children are considerably more likely to prioritize environmental protection over economic growth and be willing to pay more taxes to combat climate change.

Most of the confounding variables affect beliefs about the importance of government actions and willingness to pay for climate change action in similar ways – past exposure to climate impacts, perceived risks of future impacts, preferences for the market economy, trust in government, and beliefs that success is meritocratic positively correlate with climate action, both personal and by the government. Greater risk aversion, reliance on social media, and plans to move away in the near future also correlate positively with attitudes about climate action.

18