rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7769

# **Abstract**

Considerations of risk and vulnerability are key to understanding the dynamics of poverty in rural Malawi. This study measures vulnerability to consumption shortfalls and analyzes its sources using a two-period panel of 2,789 households, drawn from the 2010 Third Integrated Household Survey and the 2013 Integrated Household Panel Survey. The results show that in 2010 two-fifths of all households had a chance of at least 40 percent of falling below the poverty line in the future. The results show that many households in rural Malawi are vulnerable to poverty, although, as with many other studies of rural areas in other countries, much of the vulnerability is caused by chronic poverty. Nonetheless, risks, particularly rainfall and loss of off-farm employment, are also important in explaining why poor households remain poor, and why some non-poor households are more likely to fall into poverty in the next period. Household wealth and agricultural assets can protect households from falling into poverty and reduce the severity of the fall when shocks occur. However, there is little evidence to suggest that other strategies to reduce vulnerability are effective.

This paper is a product of the Poverty and Equity Global Practice 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 authors may be contacted at adelafuente@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
design based on a national sample frame developed from the 1999 Population and Housing Census. The sample is designed to be representative within urban and rural stratifications at the national and provincial levels and at the level of the country’s 69 districts. The total number of households interviewed was 13,212, and the questionnaire refers to the most recently completed agricultural season.

The 2001 Rwanda _Enquête Integrale sur les Conditions de Vie des Menages_ (EICV) was collected over 21 months from October 1999 to June 2001. The bulk of the data collection was done in the last 12 months, and therefore this paper limits the analysis to this time period (Rwanda Statistics Department, 2002). Data collection was done concurrently in all 12 prefectures of the country. The sample was selected using a two-stage stratified sample design and the sample is designed to be representative at the prefecture level. The total sample size was 5,739 rural and urban households, interviewed from July 2000 to June 2001, and the questionnaire refers to the most recently completed agricultural season.

In the analysis, we regress information on harvest sales (in kilograms and in local currency) and input use (fertilizer and hired labor) on time elapsed between the harvest and the date of the interview. We explore systematic under/over-reporting by length of recall. We examine whether reporting changes between interviews conducted further from the harvest and those completed close to harvest.

Agriculture is the dominant source of income for the rural households in these surveys. In Kenya, 82 percent of rural households were engaged in agriculture, as opposed to 22 percent that were engaged in non-farm enterprise and 31 percent where at least one member was engaged in wage labor. Differences are even larger for rural households in Malawi and Rwanda. In Malawi, 95 percent of rural households were involved in agriculture, with only 30 percent engaged in non-farm enterprises and 16 percent in wage labor. In Rwanda, 99 percent of households were engaged in agriculture, and only 13 percent in non-farm enterprises and 30 percent in
Data sources/ measurement

- 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group

_See the relevant text: “Diagnostic and treatment accuracy (knowledge) were measured using clinical vignettes focused on seven common conditions associated with the main causes of child and maternal mortality in Sub-Saharan Africa: malaria, tuberculosis, diarrhea, pneumonia, diabetes, neonatal asphyxia and post-partum hemorrhage. All seven of these are major global health challenges, with three of them – diarrhea, neonatal asphyxia, and malaria – together causing more than 180,000,000 disability-adjusted life years (Appendix Table S3). Whether providers are available to provide care at regular opening hours was assessed by verifying their presence through unannounced visits at the health facilities. Availability of drugs and functionality of equipment was assessed by visual inspections of storage facilities and consulting rooms at the health facilities._ _Clinical vignettes have been used extensively in the medical literature to assess clinical knowledge of health workers. Data from vignettes studies are directly policy relevant as they are closely linked to the quality of medical training and quality improvement programs. Validation studies show that clinical vignettes, for relatively simple scenarios such as those assessed as part of the SDI program, perform similarly to standardized patients methods. Although a health worker’s clinical vignette performance is not a measure of quality of care per se, it represents the highest level of quality care which s/he can deliver given his/her current knowledge, assuming that there are no constraints in drugs, equipment and motivations. (More details on the clinical vignettes are provided in Section S4 in the appendix.)_ _The clinical vignettes were adjusted to country-specific diagnostic and treatment protocols. All health providers assessed were asked for informed consent to participate. The enumerators presented the provider with the patient’s symptoms and prompted the provider to take the patient’s history, suggest physical examination and diagnostic tests that in principle could be conducted
may be using the “wrong” data, or a poor proxy, to measure the truth of what weather occurred where and when. This can introduce either classical or non-classical measurement error into our estimates and predictions. Second, if economists are aware that different EO products report different numbers, then we can engage in _p_ -hacking, HARKing, and other unethical behavior by choosing the EO product that gives us the results we want.

To determine the magnitude of this disparity and its impact on the use of EO data in economic contexts, we integrate nine EO geospatial weather datasets with georeferenced longitudinal household survey data. The survey data comes from six Sub-Saharan African countries and was collected under the World Bank’s Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) initiative. We assess the degree to which mismeasurement matters by modeling the relationship between weather and smallholder agricultural productivity, as captured in the LSMS-ISA survey data. We use a simple heuristic to compare coefficients from regressions of yield on weather as measured by one EO product to coefficients when weather is measured by a different EO product. Our goal is to provide guidance to researchers looking to integrate EO weather data with socioeconomic survey data regarding the degree to which their results may be dependent on their choice of remote sensing EO product.

We find that the different measurement methods by EO products affect not only the cardinality of coefficients across the products but also coefficient ordinality. The first result is unsurprising. Obviously, continuous variables measured differently by different sources will result in different effect sizes. In applied economics, we are typically not too concerned about differences in relative size (cardinality) across specifications, as we understand that different choices in data cleaning and modelling will affect the exact size of a coefficient. What we care about is getting the order correct (ordinality). That using different EO products affects the ordinality of coefficients means that EO products are not simply affine transformations of one another. One can completely reverse the ordering of coefficient size across EO products by simply changing
|48.2|***|
|Average self-employed non- agricultural income (per<br>self-employed non-agricultural worker)|511.2|643.7|25.9|***|
|Urban|913.0|1122.9|23.0|***|
|Rural|345.4|446.0|29.1|***|
|Average income, other sources (per adult)|45.5|73.4|61.3|***|
|Urban|80.9|125.8|55.6|***|
|Rural|31.0|51.8|67.4|***|
|Average remittances (per capita)|24.4|53.8|120.0|***|
|Urban|13.5|36.8|171.6|***|
|Rural|28.9|60.8|110.0|***|
|Average benefits (per capita)|2.5|10.0|293.9|***|
|Urban|1.9|6.8|255.3|***|
|Rural|2.8|11.3|304.8|***|

* Significant at ten percent; ** significant at five percent; *** significant at one percent. Note: Income sources are in rupees, constant prices of 2005, spatially deflated. Source: IHDS, 2005 and 2012.

**Table 6. Share of each income source in total income, household level**

|**Income source**<br>**(% of total household income)**|**Ye**<br>**2005**|**ar**<br>**2012**|**
50 Table 8: Topical distribution of the most-cited 50 publications of the United States‘ top-5 institutions and Europe‘s top institution

||Harvard U|World Bank|MIT|U CA,Berkeley|Chicago U|U York|
|---|---|---|---|---|---|---|
|Health and its value|2||1|1|1|5|
|Efficiency and equity|4|6|1|1|2|6|
|Determinants of health and ill-health|6|14|5|11|14|3|
|Public health|5|5|5|6|5||
|Health and the economy|5|9|8|10|2|5|
|Health statistics and econometrics|3|2|1|1|5|3|
|Demand for health and health care||3|3|3||2|
|Medical insurance|5||11|2|4||
|Supply of health services|7|1|4|1|7|3|
|Human resources|6|3|7||2||
|Markets in health care|1||1|||4|
|Economic evaluation|2||1|4|1|19|
|Total|46|43|48|40|43|50|
|Normalized Herfindahl index|0.089|0.176|0.118|0.160|0.156|0.181|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed institution was given full credit for a publication in the case of a co-authored publication, but only the first institution was credited where an author
conditions. jj−1 GGC GGC tt

- _Crypto-asset prices._ We include month-on-month price changes for two key crypto-assets: bitcoin and ether to explore the impact of prices on crypto volume. These price changes are calculated analogously to ∆E j −1.

- • _Gold prices._ Some major crypto-assets such as Bitcoin are perceived by some crypto-asset CChCCLLaaCC RRC CC

- _Gold prices._ Some major crypto-assets such as Bitcoin are perceived by some crypto-asset investors as “digital gold” or “digital stores of value” with macro hedge properties. Therefore, to explore the relationship with crypto volume and gauge whether the data bear out whether they are (imperfect) substitutes, we include month-on-month gold price changes.

- _Risk appetite_ . We use the VIX index, a U.S. stock market-based measure of investor uncertainty and risk aversion, as a proxy for global risk appetite. Higher values indicate higher levels of risk aversion. When risk appetite is high, investors may be more interested in risky investments such as crypto assets causing a rise crypto volume. The VIX index

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of Albania and Bangladesh, under the log-linear approach, and in Iraq with both methods, rent shares are a decreasing function of households’ consumption.

As a reminder to the reader, in the following analysis we report all results using also the official consumption aggregate (denoted in the tables that follow as _original_ ). The main tables are shown only for the most recent year, with the exception of shared prosperity, where both years are used, and poverty results for Bangladesh, where we show 2005 instead of 2010. This is because the 2005 HIES was used to update the official poverty line, and we were able to get hold and to modify the original code for this year.

# **4.1. Inequality**

The impact of including rent on inequality is assessed by looking at changes in the Gini index. Table 5 shows the Gini index calculated on the consumption aggregate without the rental component (first row) and the difference, in absolute value, between the initial Gini index and the value of the same indicator calculated on consumption aggregates that include imputed rents according to one of the two imputation methods, namely the self-assessment approach and the log-linear model.

Albeit always statistically significant, only in few instances the impact on inequality of including rents in the consumption aggregates is over one percentage point, although the exact magnitude of the changes varies across countries and for the same country across rent-imputation approaches. A notable exception is Iraq 2007 (Table 5, panel a), where we observe an increase of almost 3 percentage points in the case of the log-linear model relative to the case of no rent. In Albania and Peru, the increase in inequality is as large as 2 percentage points when the self-assessment approach is used. This is likely explained by the large variation introduced by selfassessed values of home-owners, who in most cases tend to overestimate the rental market price due to their attachment to the dwelling they own and in some cases they built (see Heston and Nakamura 2009 for the home-owner pride effect,
)|0.0023***<br>(0.0002)|0.0023***<br>(0.0002)|
|Observations|722,592|722,592|722,592|722,592|722,592|722,592|
|_R_<sup>2</sup>|0.6163|0.7595|0.3723|0.6106|0.7446|0.3670|
|Kleibergen-Paap rk Wald-F-stat||||115.011|115.011|115.011|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, excluding the top-3 most robotized sectors from the baseline estimation sample (automotives, rubber and plastics, metal). Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
(0.305)|
|Constant|0.934<sup>∗</sup>|1.276<sup>∗∗∗</sup>|0.457|0.645|0.935<sup>∗∗∗</sup>|0.507|
||(0.560)|(0.485)|(0.380)|(0.400)|(0.061)|(0.832)|
|Province FE|No|No|No|No|No|Yes|
|Observations|1227|1227|1123|1102|1232|1007|

_Notes:_ Authors’ analysis based on data described in the text. The table reports Probit estimates. Selective attrition has been tested for the log of dependent variables in 2006. Consumption and spendings are per adult equivalent. Sampling weights are accounted for. Clustered-robust standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 35
**Table 2. Operationalizing an Investment and Mode 3 Trade in Services Facilitation Agenda**

|**Trade in services**<br>**cost component**|**Market Failure/**<br>**Problem to be**<br>**addressed**|**Trade facilitation**<br>**agenda dimension**|**Diagnostic**<br>**indicator**|**Impact Indicators****|
|---|---|---|---|---|
|Information costs|Information<br>asymmetry|Transparency|WBG<br>governance<br>indicators|Trade/investment<br>generated|
|Transaction costs|Red tape|Process streamlining|WBG/IAB<br>indicators|Private time/cost<br>savings|
|Governance quality|Uncertainty and un-<br>predictability from<br>regulatory risk|Consistency and<br>predictability in the<br>application of<br>regulatory<br>frameworks|Political risk<br>indicators|Investment<br>retained/expanded|
|Discriminatory Policy<br>Barriers|Distortions to<br>competition|Standstill of existing<br>discriminatory<br>measures affecting<br>commercialpresence|STRI|Trade/investment<br>generated|
|Regulatory<br>Heterogeneity|Economies of scale|Inter-governmental<br>cooperation<br>promoting regulatory<br>convergence through<br>negotiation|RH Index|Trade/investment<br>generated|

** Methodologies to operationalize these impact indicators have already been developed by the WGB.

While the substantive remit of the WTO investment facilitation discussions is arguably sub-optimal in scope, it nonetheless targets a range of issues where the dissemination of best practice examples in policy design, internationally-agreed rules and strengthened coordination and implementation efforts at both the international and domestic levels
, on average, wages in the private sector are higher than the private sector wages.

# **4. Data, Definitions and Stylized facts**

Data for this study come from Round 50 and Round 55 of the all-India household survey collected routinely by the National Sampling Survey Organization (NSSO), Department of Statistics and Program Implementation, Government of India. Both Round 50 and Round 55 are so-called quinquenial surveys, which include a Consumption Survey (CS)

> 3 The difference with the Duraiswamy and Duraiswamy (1995) study is possibly a different specification of the earning functions.

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entirety of the economy - persist. Additionally, we find a statistically significant negative correlation between exit rates and the concentration of BOS in competitive industries. Our results suggest that industries with a higher presence of BOS are characterized by lower shares of young firms, lower levels of selection and higher market concentration, all of which contribute to restricting business dynamism and firm life growth (Hsieh and Klenow, 2014). However, we do not find a statistically significant relationship between the employment share of BOS and entry rates, which would corroborate the negative relationship reported for entrepreneurship activity, measured by the employment share of young firms. In terms of the labor market, a positive correlation between the presence of BOS and job creation could be reflecting the ability of these firms to grow even in an environment of overall lower business dynamics. As we have shown in section 3, firms with state participation have higher employment growth rates relative to private firms.

> 16Since no information about firm output or sales is contained in RAIS, we compute the HHI using firm-level employment.

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# **Appendix n:** Data

# A. Country coverage

The following is the list of countries covered in our study. Since the panel data set is unbalanced, we also indicate the time periods for which observations are available in each of the 70 countries.

|Countrv|1961-65|1966-70|1971-75|1976-80|1981-85|1986-90|1991-93|
|---|---|---|---|---|---|---|---|
|Algeria|||||x|x|x|
|Argentina|x|x|x|x|x|x|x|
|Australia|x|x|x|x|x|x|x|
|Austria|x|x|x|x|x|x|x|
|Bangladesh||||x|x|x|x|
|Bolivia|x|x|x|x|x|x|x|
|Brazil|x|x|x|x|x|x|x|
|Cameroon||x|x|x|x|x||
|Canada|x|x|x|x|x|x|x|
|Central African Rep.||x|x|x|x|x|x|
|Chile|x|x|x|x|x|x|x|
|Colombia|x|x|x|x|x|x|x|
|Costa Rica|x|x|x|x|x|x|x|
|Cyprus<br>|||x|x|x|x|x|
|Demnark|x|x|x|x|x|x|x|
|Ecuador|x|x|x|x|x|x|x|
|El Salvador|x|x|x|x|x|x
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# behavior in the aftermath of the financial crisis.

# Figure 5: Measures facing EU exporters by group, 1996-2015

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>Investment Related Barriers IPR NTB<br>Other (export related) Services specific Measures Tariff and Duties<br>Trade Defense Instruments<br>Number of measures<br><!-- End of picture text --> Source: Authors's calculations based on EU Market Access database.
with that of the stock markets, which are still underdeveloped in transition economies.

# **_Exit costs_**

Bankruptcy costs, and the more elusive stigma of failure, might have a large effect on the entry decision of would-be entrepreneurs. That is the reason why we have included the exit costs in the section dedicated to factors preventing firm entry.

The importance the of bankruptcy costs as an explanatory factor of entrepreneurial activity is based on the observed large differences in the perception and the cost of failure between Europe and the US. While around 50% of the Europeans surveyed in an empirical study affirmed that “one should not start a business if there is a risk it might fail”, only 25% of North-Americans thought the same.<sup>6</sup> Among the risks most

> 5 Slavica Singer et al. (2003): What makes Croatia a (non) entrepreneurial country? Global Entrepreneurship Monitor 2002.

> 6 Flash Eurobarometer 134 “Entrepreneurship”.  Done by EOS Gallup Europe upon request of the European Commission (DG XXIII), 2000, 2001 and 2002.

7
<br>20 AGO 20 GIN SEN GHACIV<br>TCDGNBBFAUGAGINLSO SEN GHACIVMRT GAB MRT GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.3285 R2 = 0.3290<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: GDP per capita is measured in PPP (constant 2017 international $). For the DOU and DB methods, GHSPOP 1km is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method. Poverty is measured using the $2.15 poverty line.

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5|50.9|46.1|47.1|47.2|
|1 child|18.2|16.8|19.0|18.1|23.9|25.5|24.3|26.5|32.7|34.6|37.6|39.3|41.7|35.7|38.3|38.2|
|2 children|41.8|35.7|35.4|35.5|48.0|43.8|46.1|44.2|54.6|56.1|54.6|58.6|61.5|58.7|56.9|57.6|
|3 and more|||||||||||||||||
|children|59.5|65.9|53.2|56.5|64.5|64.1|66.3|65.9|77.0|69.8|76.8|77.4|79.7|76.1|74.6|76.8|
|singleparent|34.5|32.9|36.1|36.8|36.0|36.7|36.9|39.2|46.0|43.9|47.1|52.7|51.0|50.3|45.9|47.0|

Source: Authors’ estimates based on HSS data.

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report to have been engaged in crime at some point.<sup>13</sup> The national government is well aware of these challenges, and established “Human Capital Development, Gender, Youth, and People Empowerment” as one of the 7 pillars in its long‐term ‘Vision 2050’ plan. This plan states that “programs and systems must be improved and expanded to encourage greater participation from youths in all organizations and institutions, and in development programs”. In addition, the Development Strategic Plan (DSP) 2010‐ 2030 sets ambitious targets to provide universal education for all, ensure that all school graduates enjoy employment opportunities, and also, to halve the rate of youth crime.

Given the emphasis on youth well‐being and crime, the evaluation was collected on a variety of behavioral and criminal characteristics, which are divided into three groups. The first set is anti‐social behavior associated with either being a victim of or engaging in criminal activity. These include hanging out with friends in the streets late at night, having friends who have engaged in a criminal activity, or frequently getting drunk. The second set is self‐reported engagement in criminal activity, such as stealing, assault (physical or verbal), alcohol or drug‐related activity (e.g., fights), trespassing and domestic violence. The final group of indicators is the reported incidence of being a victim of a criminal activity.

> 12 Derived from 2011 Census Report.

> 13 National Youth Policy 2007‐2017, National Youth Commission, 2007.

17