rafmacalaba/datause-encoder-data huggingface dataset:
We contribute to this literature by tracking monthly changes in household expenditure and income following the Kerala floods. The results in section 6 show a persistent decline in household expenditure, particularly non-essential expenditure, despite a quick recovery and subsequent increase household income. The flood also affected the composition of household debt and savings. In section 7, we show that households were more likely to borrow for housing and medical purposes, and less likely to borrow for consumption.

# **4. Data, Samples, and Summary Statistics**

Our analysis employs numerous district-level and household-level variables from various sources. This section describes the data, discusses the sample selection, and reports summary statistics.

# **4.1 Data**

# _Nighttime Lights_

Nighttime lights are a widely used proxy for aggregate economic activity.<sup>14</sup> Felbermayr et al. (2022) use nighttime lights to investigate the impact of weather anomalies on economic activity. In India, they have been used to analyze the spatial impact of demonetization (Chodorow-Reich et al. 2020), regional convergence (Chanda and Kabiraj 2020), and the heterogeneous impact of the COVID-19 pandemic (Beyer, Franco-Bedoya, and Galdo 2021, Beyer, Jain, and Sinha 2021). To translate changes in nighttime lights into changes in economic activity, we rely on the quarterly elasticity estimated by Beyer, Hu, and Yao (2022). Although nighttime lights data are very noisy over time, they are useful for difference-indifference analyses since most of the noise is common across the treated and untreated units.

We extract district level nighttime light data from the Visible and Infrared Imaging Suite Day Night Band (VIIRS-DNB) Cloud Free Monthly Composites (version 1) provided by the Earth Observation Group at the Colorado School of Mines.<sup>15</sup> The monthly composite

> 14For a summary of the literature, see Donaldson and Storeygard (2016).

> 15We employ the VIIRS Cloud Mask (VCM) configuration starting from April 2012. For information on the different versions, visit https://eogdata.mines.edu/products/vnl/. Due to a wider radiometric detection

10
recognition of family land is automatic, i.e. does not require any intervention by local governments, less than one third of the individuals asked know about this. As women play a major role in cultivation, the fact that more than two thirds of women are unaware of their legal land rights to land could indeed have far-reaching implications for investment and land use decisions that will in turn translate into lower levels of productivity. All of the descriptive evidence suggests that dissemination to overcome the ignorance surrounding key provisions of the Land Act could have a significant impact in a number of respects. Below we explore the extent to which this is borne out by more rigorous econometric evidence.

# **4. Econometric results**

Exploring the impact of tenure security and transferability on investment, productivity, and land values provides not strong support for our hypotheses that disaggregating different types of land rights (i.e. tenure security and transferability) can provide important insights. It also suggests that the legal change can provide very large benefits. To the extent that lack of dissemination by the government and the corresponding lack of knowledge by households have thus far prevented full realization of the benefits from this Act, efforts to better disseminate the new law will have a high return.

# **4.1 Factors affecting visible and non-visible investments**

First stage results for land transfer rights, using the modes of acquisition as identifying instruments, are reported in appendix table 1. They point towards a significant impact of the mode of acquisition on the transfer rights for any given parcel (omitted reference categories are _mailo_ tenure and purchased land, respectively). Plots that had been inherited or just occupied as well as house plots (or those closer to home) are significantly more likely to be transferable only with outside approval, that plots under _mailo_ or freehold tenure and those closer to urban centers and infrastructure (electricity) are more likely not to be subject to such restrictions, while rented plots are significantly less likely to be transferable at all. Households’ level of education and their knowledge about the land Act make it more likely for them to be aware of the conditions imposed on transferability of land.

Table 4 presents results from the second-stage AGLS regression to identify determinants of land-related investment, measured by a dummy for whether or not any trees were planted (column 1), measures of soil conservation were undertaken (column 3), or the log of the number of trees planted per acre (column 5). Signs of the coefficients on parcel characteristics such as distance to the house, land quality, size and topography of the plot are significant, indicating that long-term investment is more likely on parcels that are close to the homestead, larger in size, steep or undulating rather than completely flat, and have at least medium levels of land quality.

15
# **3. Costs and benefits of solutions**

Various experiments have been undertaken to improve test scores in Kenyan schools (Table 1). While scholarships for girls, teacher incentives and textbooks are significant, the most effective and cost-effective are the scholarships. Glewwe (1999) shows that in terms of present discounted values of various school improvements that installing blackboards is the most costeffective, followed by repairing leaking classrooms. Investments in textbooks are not shown to be cost-effective. Some interventions on the health side have been undertaken to improve education outcomes. The case of deworming in Kenya proved to have an impact not just in health, and on schooling. The cost is approximately $5 per DALY averted and a total number of 649 DALYs averted as a result of the program (Kremer _et al_ . 2004).

<u>Table 1: Cost-effectiveness of Various Kenya Primary School Interventions</u>

|Project|Average<br>test score<br>gain,<br>years 1-2|Cost/pupil|Cost/pupil<br>per 0.1<br>s.d. gain|Cost/pupil per<br>0.1 s.d. gain<br>adjust for<br>deadweightloss|Cost/ pupil<br>per 0.1 s.d.<br>gain, adjust<br>for<br>deadweight<br>loss &<br>transfers|
|---|---|---|---|---|---|
|Girls scholarship||||||
|Busia & Teso Districts|0.12 s.d.|$4.24|$3.53|$4.94|$1.41|
|BusiaDistrict|0.20 s.d.|$3.55|$1.77|$2.48|$0.71|
|Teacher incentives|0.07 s.d.|$2.39|$3.41|$4.77|$1.36|
|Textbook provision|0.04 s.d.|$1.50|$4.01|$5.61|$5.61|
|Deworming project|≈0|$1.46|∞|∞|∞|
|Flip chart provision|≈0|$1.25|∞|∞|∞|

Notes: Costs in nominal US$ at time of program (1996-2002); education budget cost effectiveness calculation yields upper bound on true social cost (column 4), while lower bound generated by treating entire payment as transfer (column 5) Source: Kremer _et al_ . (2004), and Glewwe _et al_ . 2004, 2003, 1997; Miguel and Kremer 2004) Perry Preschool generates for every dollar invested over $7 in benefits (Barnett 2004). Based on present value estimates, about 80 percent of the benefits go to the general public, yielding a 12 percent rate of return for society. The Abecedarian Project produced more favorable findings for children who participated in the program compared to children who did not, revealing a benefit of $3.78 for each dollar invested (Barnett and Masse 2006). Cost/benefit analyses may understate the true, long-term impact of early care and education because they do 8
PPR Working Paper Series

|IU1|Author|Date|Contact<br>for aper|
|---|---|---|---|
|WPS323 The Old and the New in Heterodox<br>Stabilization Programs: Lessons<br>from the 1960s and the 1980s|Miguel A. Kiguel<br>Nissan Liviatan|December 1989|R. Luz<br>61588|
|WPS 324 Ethical Approaches to Family<br>Planning in Africa|F. T. Sai<br>K. Newman|December 1989|S. Ainsworth<br>31091|
|WPS325 Manufacturers' Responses to Infra-<br>structure Deficienicies in Nigeria|Kyu Sik Lee<br>Alex Anas|December 1989|L. Victorio<br>31015|
|WPS326 Do Exporters Gain frorn VERs?|Jaime de Melo<br>L Alan Winters|||
|WPS327 Making Noisy Data Sing: Estimating<br>Production Technologies in<br>Developing Countries|James R. Tybout|||
|WPS328 Europe, Middle East, and North<br>Africa (EMN) Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS329 Laiin America and the Caribbean<br>(LAC) Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS330Africa Region Population<br>Projections, 1989-90 Edition|Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|Nolember 1989|S. Ainsworth<br>3'091|
|WPS331 Asia Region Population Projections<br>1989-90 Edition|, Rodolfo A. Bulatao<br>Eduard Bos<br>Patience W. Stephens<br>My T. Vu|November 1989|S. Ainsworth<br>31091|
|WPS332 India: Effective Incentives in<br>Agriculture: Cotton, Groundnuts,<br>Wheat, and Rice|Ashok Gulhati|||
|WPS333 Secondary Market Prices under<br>Alternative Debt Reduction<br>Strategies: An Option Pricing<br>Approach with an Application to Me|Stijn Claessens<br>Sweder van Wijnbergen<br>xico|||
|WPS334 Built-in Tax Elasticity and the<br>Revenue Impact of Discretionary<br>Tax Measures: An Ecornometric<br>Estimation Method with Application<br>to Malawi and Mauritius|Jaber Ehdaie|||
# **Figure 6**

**Co-movement of Brazil C Bond Prices and the Dow Jones Index Evidence of Global Factors’ Influence on Brazilian Asset Prices**

<!-- Start of picture text --> 90 12000<br>DJIA BR C Bond Price<br>(right scale)<br>80 11000<br>70 10000<br>60 9000<br>50 8000<br>40 7000<br>1998 1999 2000 2001 2002<br><!-- End of picture text --> Source: World Bank staff calculations 21. Faced with mounting difficulties in rolling over the domestic debt, the central bank redeemed a fraction of debt falling due by printing money. Consequently, the monetary base expansion exceeded nominal GDP growth (Figure 7). The monetary effect of public domestic debt redemptions during the second semester of 2002 reached the tenor of 30 percent of base money (Figure 8). It is crucial to point out, however, that the positive monetary expansion due to the treasury’s operation began in the second semester of 2001 and could have been interpreted as a leading indicator of the more turbulent episodes that were to unravel in mid 2002.

**Figure 7 Figure 8 Money Base as a Share of GDP Monetary Impact of Treasury’s (seasonally adjusted data Operations 1999- 2003 (ratio to the monetary base**

<!-- Start of picture text --> 5.0 .4<br>.3<br>4.5<br>.2<br>4.0<br>.1<br>3.5<br>.0<br>3.0 -.1<br>2.5 -.2<br>-.3<br>2.0 1999 2000 2001 2002 2003<br>95 96 97 98 99 00 01 02 03<br>Source: World Bank staff calculations based on Bacen Source: World Bank calculations based on Bacen<br>data data<br><!-- End of picture text --> 22. Moreover, base money grew in lockstep with the faster depreciation of the currency. This pressured inflation, which accelerated between June and December, reaching a peak of 5.8 percent per month in November (Figure 9). Monetary growth and rising inflation increased the government’s revenue from money creation up to the 11
- 30 -

# - + (a#/z)2(7 B(POO27-1,26+1) 276B(PO,27,26)

+ 6<sup>2</sup> B(PO,27+l126-1)] (A14) k rn-i n-1 - where B(k,m,n) pI (l-p) dp. (Several software packages allow one to 0

> evaluate this using incomplete beta functions.) Thus, given (j, a, 7, 6) for any region, the FGT poverty measures for any poverty line are calculated from (A12), (A13) and (A14). The probability densities at the poverty line (as required by Propositions 1 and 2 and for calculating 70) are readily estimated using the fact that f(z) - l/(L"(Po)).
**Table 5: Robotics, new products and initial quality gap**

|||New Pro|ducts||N|ew Products|andQuality|Gap|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Outcome:||Quali|ty|||Qu|ality||
||OL|S|I|V|O|LS||IV|
|Robots|0.022*||0.110***||0.007||0.138***||
||(0.012)||(0.039)||(0.011)||(0.029)||
|Robots * New Product|0.080***||0.135***||-0.005||-0.012||
||(0.015)||(0.028)||(0.017)||(0.026)||
|Robots * Quality Gap|||||**0.065*****<br>**(0.005)**||**0.112*****<br>**(0.008)**||
|Robots – Developing||0.039*||0.250***||0.007||0.171***|
|||(0.022)||(0.091)||(0.017)||(0.060)|
|Robots – Developing * New Product||0.081***||0.094***||-0.007||-0.009|
|||(0.018)||(0.036)||(0.020)||(0.033)|
|Robots – Developing * Quality Gap||||||**0.072*****||**0.099*****|
|||||||**(0.007)**||**(0.008)**|
|Robots – Developed||0.006||0.038||0.008||0.126***|
|||(0.010)||(0.025)||(0.013)||(0.030)|
|Robots – Developed * New Product||0.049*||0.164***||-0.003||0.019|
|||(0.029)||(0.054)||(0.028)||(0.048)|
|Robots – Developed * Quality Gap||||||**0.052*****||**0.129*****|
|||||||**(0.007)**||**(0.016)**|
|Kleibergen-Paap F statistic|||61.0|16.4|||41.6|11.1|
|# Observations|897,260|897,260|897,260|897,260|897,260|897,260|897,260|897,260|

_Note: Quality gap reflects the initial distance from the “quality frontier. The quality frontier is defined within a given product as the top 10% highest exporters quality. The quality gap is defined as the difference between export quality and the frontier export quality in the first year. New products are defined as HS 10digit products first exported by a given country since 2000 and not previously exported during the 10 years of pre-sample data, 1990-1999. Robots reflects log robot stock. The instrument reflects robot diffusion in (initial) foreign export partners, in other world regions, defined using 2000 export flows. Quality refers log quality, estimated via Khandelwal et al (2013). Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The regressions also includeexporter*10-digit product and exporter*year fixed effects. The first stage has been omitted for brevity. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._ 31
Policy Research Working Paper 7296

# **Abstract**

This paper informs the national and international policy discussions related to the adoption of the United Nations Reducing Emissions from Deforestation and Forest Degradation Programme. Effective program instruments must carefully consider incentives, opportunity costs, and community interactions. A choice experiment survey was applied to rural Ethiopian communities to understand respondents’ preferences toward the institutional structure of the program contracts. The results show that respondents have particular preferences about how Reducing Emissions from Deforestation and Forest Degradation programs are structured with regard to the manner in which the payments are divided between the households and the communities, the restrictions on using grazing land, and the level of payments received for the program. Surprisingly, restrictions on firewood collection do not significantly impact contract choice. The paper further analyzes the structure of the preferences by using attribute interaction terms and socio-demographic interaction terms. The analysis finds significant regional variation in preferences, indicating that Reducing Emissions from Deforestation and Forest Degradation should be tailored to specific regions. Finally, the marginal willingness to pay for attributes is calculated using the traditional preference space approach, as well as the more recent willingness-to-pay approach.

This paper is a product of the Environment and Energy Team, Development Research Group. 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://econ.worldbank.org. The lead author may be contacted at sdissan2@gmail.com _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
organized for the concession of the Athens airport<sup>6</sup> . In South Africa, PPP guidelines recommend inversely reimbursing a portion of bids’ preparation costs only to bidders submitting compliant bids.

## **Box 2: Timeline of the Ashkelon BOT project in Israel**

<mark>In 2001 the Water Desalination Authority (WDA) launched a 24-year and 11-month BOT tender for the construction and the operation of a 50 million m</mark><sup>3</sup> <mark>desalination plant in Ashkelon. The plant capacity was to be off-taken by the government of Israel. After a pre-qualification stage, three major international players</mark> involving local firms were selected to participate in the final bidding stage as outlined below: <mark>- February 2001 (end of Month 1): Issuance of the final BOT agreement</mark> <mark>- Month 4: Initial date of Bid submission</mark> <mark>- Mid- month 7: Bid submission</mark> <mark>- Month 8: Best And Final Offer (BAFO) and the project was awarded to Vivendi Water (now Veolia) the very same day</mark> <mark>- Month 11: Selection of the arranger</mark> <mark>- Month 12: Extension of the project scope to 100,000,000 m</mark><sup>3</sup> <mark>by authorities</mark> <mark>- Month 13: Signature of the BOT Agreement; finalization of detailed design by the project’s sponsors - Month 25: Financial close; start of construction.</mark> _Source: Veolia Water (2003)_

# _2. Certainty and predictability of the bidding process_

Bidders dislike uncertainties. In general, most of the uncertainties observed in bidding processes stem from loopholes included in bidding procedures or documents. Bidders spend time and resources to review bidding documents while preparing their bids. The more bidding documents include loopholes or inconsistencies, the costlier becomes the preparation of compliant bids. Furthermore, bidders also exploit these inconsistencies and loopholes to their advantage as illustrated in Box 3.

## **Box 3: Telecom auction in Turkey**

<mark>In 2000, Turkey auctioned two telecom licenses</mark> _<mark>sequentially</mark>_ <mark>with an additional twist that set the reserve price for the second license equal to the selling price of the first. One firm then bid far more for the first license than it could possibly be worth if the firm had to compete in the telecommunications market with a rival holding a second license. The firm had rightly figured out that no rival would be willing to bid that high for the second license which therefore remained unsold – leaving the first firm without a rival operating the second license. The main uncertainty in this bidding process came from the auction design: sequential</mark> award of two licenses but at the same price.

_Source: adapted from Klemperer (2002 b)_

> 6 The initial capital costs for this project were estimated at US$750 million (World Bank, 1998) and the government reimbursed up to US$2million.

8
eligible firms obtained from the country’s statistical office<sup>6</sup> using stratified random sampling with replacement to generate a sample representative of the whole non-agricultural private economy (so fully government-owned firms are excluded from the sampling universe) in the country. The surveys are stratified according to three criteria: _Sector of activity_ (population of industries include manufacturing sectors, construction, services, transport, storage, communications, and computer and related activities), _Firm size_ (the strata include small firms (5-19 employees),<sup>7</sup> medium firms (20-99 employees), and large firms (100 or more employees)), and _Geographical location_ (selected based on centers of economic activity in the country).

While the ES have been produced since 2002, we restrict our sample to surveys administered during 2006-2012 since these provide sampling weights that address the varying probabilities of selection across different strata and are thus indispensable to making assertions about the whole population.<sup>8</sup> For countries that were surveyed twice during this period, we retain the most recent survey data for each country. Our results are also robust to retaining data for the year with the largest number of firms surveyed.

**Establishment Size** is the number of permanent, full-time employees in the

establishment where permanent, full-time employees are defined as all paid employees that are contracted for a term of one or more fiscal years and/or have a guaranteed renewal of their employment contract and that work 8 or more hours per day. The survey asks establishments to report the employment numbers at the end of the last fiscal year. We compute the average

> 6 The master list of firms is sometimes obtained from other government agencies such as tax or business licensing authorities. In some cases, the sampling universe is generated from lists maintained by the Chamber of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration.

> 7 We have a few firms in the sample that report having less than 5 employees. All our results are robust to dropping these firms. Our results are also robust to dropping firms that started out as informal enterprises and were formally registered after starting operations.

> 8 Most surveys contain three sets of weights – strict, median, and weak weights depending on the eligibility criteria used to construct the sample universe. The survey implementation manual recommends the use of median weights for cross-country comparisons which is what we use in this paper.

9