rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9867

# **Abstract**

This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially linked with a range of geospatial weather data sources and related metrics. The paper provides systematic evidence on measurement error introduced by (1) different methods used to obfuscate the exact GPS coordinates of households, (2) different metrics used to quantify precipitation and temperature, and (3) different remote sensing measurement technologies. First, the analysis finds no discernible effect of measurement error introduced by different obfuscation methods. Second, it finds that simple weather metrics, such as total seasonal rainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-run average or growing degree days, in a broad range of settings. Finally, the analysis finds substantial amounts of measurement error based on remote sensing products. In extreme cases, the data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature drawn from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. The paper concludes with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data.

This paper is a product of the Development Data Group, Development Economics. 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 tkilic@worldbank.org and smurray@worldbank.org.

_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
of eqn (7) are the predicted values in the event that only the x's change or only the fl's change. Thus, if for the richest three quartiles, the ifs were to change between 1998 and 2015 as they did in the period 1993-98, but the x's stayed at their 1998 values, the U5MR would fall from 34.0%o in 1998 to 30.1%o in 2015. If, by contrast, the ifs were to remain unchanged at their 1998 values, but the x's were to grow (or decline) at similar rates to those observed over the period 1993-98, the U5MR for the richest three quartiles would fall to 21.6%o. The net effect of _both_ sets of changes is to reduce the U5MR by just under two thirds below its 1990 value. These two sets of changes would, in other words, ensure that among the richest three quarters of children in Vietnam the MDG for under-five mortality would be hit.

In the case of the poorest quartile, the picture is quite different. Under the samne two sets of assumptions as made above for the richest three quartiles, the U5MR among the poorest quartile would actually rise by 78 percent--a reduction in mortality due to beneficial changes in the _if_ s, being more than offset by deleterious changes in the x's. The prospects for the poor look somewhat better in Scenario B where they are assumed to experience the same growth rates in the x's as experienced by the nonpoor. But even in this case, the under-five mortality rate among the poorest quartile would barely change from its 1990 value. In both scenarios C and D, the prospects for the poor look decidedly better. In scenario C, where the poor are assumed to experience the same growth rates as in scenario A but are assumed to face the coefficient vector of the nonpoor, a 53 percent reduction in the under-five mortality rate between 1990 and 2015 is predicted. A 61 percent reduction is predicted in scenario D where the poor are assumed to enjoy the nonpoor's growth rates and to face the nonpoor's coefficient vector. In both scenarios A and B, what holds the poor back are the negative coefficients on the water and antenatal visit variables. Caution ought to be exercised in interpreting these negative coefficients too literally, but the results for contrast between the results for scenarios A and B, on the one hand, and scenario C and D, on the other, do point towards the importance of ensuring that poor children benefit more from water anld from antenatal visit programs than they appear to at present.

27
of diesel at Amalekhgunj depot, the main storage and distribution facilities of imported petroleum products in Nepal, was US$0.89 per liter in 2013 excluding any taxes and subsidies. The production cost of jatropha-based biodiesel is higher than the price of diesel when jatropha yield is lower than 8 tonnes per hectare.

# **Figure 5: Comparison of production costs of biodiesel with wholesale price of diesel in Nepal (US$/liter)**

<!-- Start of picture text -->
1.7<br>1.2<br>Wholesale price of diesel 0.89 US$/liter<br>0.8<br>Yield: 3t/ha Yield: 5t/ha Yield: 10t/ha<br><!-- End of picture text -->

If biodiesel yield is 3 tonnes per hectare, biodiesel would be competitive with diesel only at twice the reference price we are considering here, everything else remaining the same. Similarly, biodiesel would be competitive at jatropha yield of 5 tonnes per hectare for a diesel price 35% higher than our reference price.

Based on Figures 4 and 5 it can be concluded that jatropha-based biodiesel would not be economically attractive to biodiesel producers nor to jatropha seed farmers unless the jatropha seed yield is relatively high, more than 8 tonnes per hectare. Note however that the economic analysis presented above does not account for some additional benefits such as revenue from byproducts (e.g. glycerol, jatropha cake), or the potential value of carbon credits from replacing fossil fuels. Moreover, we have considered use of arable lands with market rents for the lands. Several studies (e.g. Achten et al., 2010; FACT Foundation, 2010; Becker and Makkar, 2008; Kumar and Sharma, 2008; Gübitz et al., 1999) claim that jatropha can be grown in low-quality lands (or so called marginal lands) where 17
**Table 4: Cross-country variability in access to infrastructure explained by country determinants - 2012**

||Water|Toilet|Sewers|Electricity|Refrigerator|Washing<br>machine|TV|Landline<br>phone|Cell<br>phone|Computer|Internet|Car|Motorcycle|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|ln GDP pc|0.4300**|0.735***|-0.7500|0.243***|0.745***|1.4280|-0.0898|0.2650|-0.1790|-0.0937|0.3230***|0.2280**|0.4470|
|Gini|0.0075|-0.0053|0.0090|0.0018|0.0148|0.0176|-0.0051|0.0051|0.0077*|0.0001|0.0097**|0.0018|0.0067|
|Density|-0.0011***|-0.0002|0.0000|0.0000|0.0002|0.0000|-0.0001|0.0000|0.0002|-0.0004|0.0003*|-0.0001|-0.0003|
|Urbanization|-0.0031|-0.0078*|0.0185|-0.0018*|-0.0031|-0.0104|-0.0016|-0.0036|0.0023|0.0074***|0.0011|-0.0034|-0,0015|
|Observations|1,188|1,188|1,188|1,188|1,188|990|1,188|1,089|1,188|1,188|1,089|1,089|990|
|R<sup>2</sup>|0.52|0.18|0.17|0.20|0.29|0.41|0.04|0.03|0.15|0.19|0.14|0.02|0.20|

29
_Step 4: Calculate attractiveness of mitigation alternatives_ . The attractiveness of mitigation is calculated as the difference between the benefits to each interested party and the upfront costs of mitigation. With respect to lifelines, the alternatives involve a degree of outage or serviceability over a defined time horizon. The social discount rate is then used to convert future benefits and costs into a net present value (NPV) or benefit-cost (B/C) ratio. A mitigation measure is considered attractive if the NPV is positive or, equivalently, if the B/C ratio is higher than unity.

_Step 5: Choose the best mitigation alternative_ . The best alternative among mutually exclusive mitigation alternatives is the one with the highest NPV or, equivalently, the highest B/C ratio.

# **3.  Application to the Bogotá Disaster Vulnerability Reduction Program**

The World Bank-financed Bogotá Disaster Vulnerability Reduction Project is a five-year project with a World Bank loan of US$80 million, and a contribution of US$80 million from the District of Bogotá. The purpose of this project is to reduce the city’s exposure to adverse natural events by strengthening local capacity to manage disaster risk and reduce vulnerability of key infrastructure. By the time of program completion both physical and financial vulnerability to adverse natural events should be measurably reduced.

This project addresses five lines of action outlined in the city’s 10-year plan: (a) risk identification, (b) risk reduction, (c) institutional strengthening, (d) risk prevention and awareness, and (e) financial coverage.

The objective of the Risk Identification component is to enhance the capacity of the District of Bogotá to identify and monitor risks. The component will help the District better target its investments and identify potential calamities before they occur. Activities include studies for hazard identification (for example, floods, geotechnical and seismic risks), vulnerability assessments (for example, assessment of substandard housing and public buildings), and risk management (for example, probability of loss of life when vulnerability is not addressed), and studies for hazard mapping, which involve equipment identification to gather information on earthquakes, floods, and landslides.

The Risk Reduction component aims to complement the city’s existing risk reduction efforts to reduce the vulnerability of critical facilities and lifeline infrastructure. The purpose of this component is to save lives by ensuring the continued functioning of such facilities in the event of adverse natural or technological catastrophe. The component also supports the implementation of nonstructural and functional mitigation measures for the continuity of service during and after emergencies. Activities under this component include seismic mitigation through the development of engineering designs and retrofitting or construction works for public buildings to meet the latest seismic standards as defined in Law 400, including hospitals, schools, and fire stations.

7
# **4.   Evaluating the impact of infrastructure on total factor productivity (TFP)**

Escribano and Guasch (2005, 2008) relate infrastructure and other IC and C variables with firm-level productivity (TFP) according to the following observable fixed-effects system of equations: where, _Y_ is firms’ output (sales), _L_ is employment, _M_ denotes intermediate materials, _K_ is the capital stock, _INF_ is a time-fixed vector of observable infrastructure variables, _IC_ is a time-fixed effect vector of other investment climate and other control variables, and _D_ is a vector of industry and year dummies.

The usually unobserved time fixed effects (<sup>_a_</sup> _i_<sup>) of the TFP equation (4.1b) is here proxy by the</sup> set of observed time-fixed components **INF** and **IC** variables of (4.1c) and a remaining unobserved random effect (<sup>ε</sup> _i_<sup>). The two random error terms of the system,ε</sup> _i_<sup>and</sup> _wit_ , are assumed to be conditionally uncorrelated with the explanatory L, M, K, **INF** and **IC** variables<sup>10</sup> of equation (4.2): ′ ′ ′ log _Yit_ = α _L_ log _Lit_ + α _M_ log _M it_ + α _K_ log _Kit_ + α _INF INFi_ +α _IC ICi_ +α _DDi_ + α _P_ + _uit_ . (4.2) Therefore, the regression equation (4.2) is representing the _conditional expectation_ plus a composite RE error term equal to _uit_ = ε _i_ + _wit_ .

Providing reliable and robust estimates of the impact of infrastructure on productivity is not a straightforward task. First, because the functional form of the PF is not observed and there is no available single salient TFP measure. Second, there is an identification issue separating TFP from PF. When any PF inputs are influenced by unobserved common causes affecting productivity—such as a firm’s fixed effects—there is a simultaneous equation problem in equation (4.1a). Third, we could expect that several IC variables have at least some degree of endogeneity, questioning therefore the conditional lack of correlation of (4.2). In what follows of this section, we briefly review the solutions to these questions suggested in Escribano and Guasch (2005, 2008) and Escribano et al. (2008).

## _Estimating infrastructure’s impact on productivity_

TFP or multifactor productivity refers to the effects of any variable different from the inputs—labor (L), intermediate materials (M), and capital (K)—affecting the production (Y) process. Since there is no

> 10 Under this formulation (and other standard conditions) the OLS estimator of the productivity equation (4.2) with robust standard errors is consistent, although a more efficient estimator (GLS) is given by the random effects (RE) estimator that

> takes into consideration the particular covariance structure of the error term, ε _i_ + _wit_ , which introduces a certain type of heteroskedasticity in the regression errors of (4.2).

21
Policy Research Working Paper 9842

# **Abstract**

Spatial difference-in-differences analysis is used to study the impacts of a large-scale development intervention aimed at improving energy efficiency in Malawi. The estimation strategy takes advantage of the geographical variation in the implementation of different project components and is based on a combination of remote-sensing (satellite) data and national household survey data. The results suggest that a combination of demand-side and supply-side interventions was associated with a statistically significant increase in electricity access, a decrease in the frequency of blackouts, and a switch from traditional fuels to electricity as the main source of energy for lighting (but not for cooking). At the same time, there is no evidence that the intervention caused households to pay more for electricity. The results are consistent with an emerging view in the literature that there are synergies between energy efficiency and energy access, especially in places where the bottleneck to wider electricity access is limited electricity generation capacity rather than the cost of connecting more clients to the grid.

This paper is a product of the Independent Evaluation 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://www.worldbank.org/prwp. The authors may be contacted at rnarayanan1@ifc.org, dnaeher@worldbank.org, and vziulu@ifc.org.

_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
# The Social Tax: Redistributive Pressure and Labor Supply

Eliana Carranza, Aletheia Donald, Florian Grosset, and Supreet Kaur<sup>∗</sup>

> ∗Carranza: World Bank (ecarranza@worldbank.org); Donald: World Bank (adonald@worldbank.org); Grosset: Columbia University (fg2429@columbia.edu); Kaur: University of California at Berkeley and NBER (supreet@berkeley.edu). This paper greatly benefited from comments by Michael Best, François Gerard, Jessica Goldberg, Pamela Jakiela, Sylvie Lambert, Guilherme Lichand, Karen Macours, Owen Ozier, Léa Rouanet, Golvine de Rochambeau, Simone Schaner, Krzysztof Zaremba, and various seminar participants. Julia Buzan, Oumar Koné, Tiphaine Forzy, Chris Tullis, Ambika Sharma, Prathyush Parasuraman, Shelby Carvalho and Cécile Delcuvellerie provided superb research assistance. We gratefully acknowledge financial support from the World Bank’s Umbrella Facility for Gender Equality, the World Bank’s Jobs Umbrella Multidonor Trust Fund, and the National Science Foundation (Kaur’s CAREER award SES 1848452). We thank Innovations for Policy Action (IPA), especially Nicolò Tomaselli, Henriette Hanicotte, Samuel Kembou Nzalé, Mireille Nuguhe Gbagbo and Augustin Kouadio for assistance with implementation. This project is a product of the World Bank’s Africa Gender Innovation Lab and Jobs Group. The findings, interpretations, and conclusions expressed in this paper do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Research approved by IPA IRB. AEA RCT Registry ID: AEARCTR-0003821. All errors and omissions are our own.
**Table 1. Number of observations for savings related measures, ICA data**

|Year|Operating Income to sales|Any Investment|IK|Retained Earnings<br>(Investment)|Retained Earnings<br>(Working Capital )|
|---|---|---|---|---|---|
||Manufacturing|||||
|2002|691|977|168|0|0|
|2003|875|977|273|716|977|
|2004|918|996|309|0|0|
|2005|873|996|198|995|995|
|2006|1002|1156|362|0|0|
|2007|1019|1156|458|1154|1152|
|**Total**|**5378**|**6258**|**1768**|**2865**|**3124**|
||Services|||||
|2004|223|0|0|0|0|
|2005|224|343|0|343|343|
|2006|335|374|0|0|0|
|2007|250|374|0|372|372|
|**Total**|**1032**|**1434**|**0**|**715**|**715**|

**Table 2. Determinants of Financial Indicators and Government Ownership, ICA data**

|Dependent<br>variables|1<br>Usage of<br>Credit<br>Products|2<br>No<br>Demand|3<br>Rejected|4<br>Savings<br>Account|5<br>Finance<br>Obstacle|6<br>Macro<br>Obstacle|7<br>Policy<br>Obstacle|8<br>Government|
|---|---|---|---|---|---|---|---|---|
|Small size|-0.20***|-0.06***|0.03***|-0.20***|0.06***|-0.08***|0.00|-0.06***|
||(0.000)|(0.005)|(0.000)|(0.000)|(0.005)|(0.000)|(0.973)|(0.000)|
|Foreign|0.09***|0.04|-0.01|0.10***|-0.06|-0.03|0.01|-0.01***|
||(0.002)|(0.222)|(0.274)|(0.001)|(0.153)|(0.327)|(0.891)|(0.003)|
|Exporter|0.10***|-0.01|0.01|0.14***|-0.01|0.08***|0.04**|0.00|
||(0.000)|(0.757)|(0.555)|(0.000)|(0.838)|(0.000)|(0.050)|(0.267)|
|Manufacturing|0.03|0.07***|-0.43***|-0.12***|0.01|-0.02|0.08***||
||(0.226)|(0.004)|(0.000)|(0.000)|(0.673)|(0.401)|(0.008)||
|Government|0.01|0.05|-0.00|0.18***|-0.08|-0.19***|-0.23***||
||(0.709)|(0.298)|(0.822)|(0.000)|(0.157)|(0.000)|(0.000)||
|Operating|||||||||
|<br>Income to Sales|-0.01|0.05*|-0.02**|0.06***|-0.02|0.00|-0.03|-0.01|
||(0.801)|(0.080)|(0.022)|(0.006)|(0.416)|(0.929)|(0.244)|(0.147)|
|Year 2005|-0.05***|-0.05**|0.03***|0.05**|0.02|-0.06***|-0.01|0.01**|
||(0.004)|(0.023)|(0.000)|(0.022)|(0.465)|(0.005)|(0.535)|(0.020)|
|Year 2007|-0.07***|0.10***|-0.07***|0.11***|0.06**|0.04*|-0.12***|0.00|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.012)|(0.063)|(0.000)|(0.312)|
|Observations|3237|3237|3237|3222|2195|3197|3199|3237|

Note: all regressions estimated by Probit.

33
POLICY RESEARCH WORKING PAPER 2437

# **Summary findings**

Vulnerability is an important aspect of households' experience of poverty. Many households, while not currently in poverty, recognize that they are vulnerable to events-a bad harvest, a lost job, an illness, an unexpected expense, an economic downturn-that could easily push them into poverty.

Most operational measures define poverty as some function of the shortfall of current income or consumption expenditures from a poverty line, and hcnce mcasure poverty only at a single point in time.

Pritchett, Suryahadi, and Sumarto propose a simple expansion of those measures to quantify vulnerability to poverty. They define vulnerability as a probability, the risk that a household will experience at least one episode of poverty in the near future. A household is defined as vulnerable if it has 50-50 odds or worse of falling into poverty.

expenditures below which a household is vulnerable to poverty. The VPL allows the calculation of a "headcount vulnerability rate" (the proportion of households vulnerable to poverty), a direct analogue of the "headcount poverty rate." The authors implement this approach using two sets of panel data from Indonesia. First they show that if the poverty line is set so that the headcount poverty rate is 20 percent, the proportion of households vulnerable to poverty is roughly _30-50_ percent. In addition to the 20 percent currently poor, an additional 10-30 percent of the population is at substantial risk of poverty.

They illustrate the usefulness of this approach for targeting by examining differences in vulnerability between households by gender, level of education, urban-rural residence, land-holding status, and sector of occupation of the head of household.

Using those definitions, they calculate the "vulnerability to poverty line" (VPL) as the level of This paper-a product of the Environment and Social Development Sector Unit, East Asia and Pacific Region-is part of a larger effort in the region to develop a national poverty reduction strategy for Indonesia. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washi[ngton, DC 20433. Please contact Patricia Sader, room MC3-556, telephone 202-473-3902, fax 202-522-1153, emnail address psader@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. Lant Pritchett may be contacted at lant_pritchett@harvard.edu. September 2000. (31 pages) _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 represen_ **_t_** _he view of the World Bank, its Executive Directors, or the countPtes they represent._ Produced by the Policy Research Dissemination Center