rafmacalaba/data-use-mentions-extended huggingface dataset:
insensitive with respect to the field shape. They also show that field area measurement errors can be limited if an appropriate combination of operator speed and GPS acquisition rate is selected.” Fasbender and Lucau (2012) also find that plot shape as well as plot size affects GNSS area measurement error. In their synthetic simulation of four distinct parcel shapes (square, rectangular, elongated narrow rectangle, and irregular polygon), with simulations for areas from 1 m<sup>2</sup> to 10 ha, they find that the variance of the measurement on the elongated rectangle and irregularly shaped polygon are the most amplified. Specifically, they suggest that the error on such irregular parcels, which are common amongst low-income regions ’ small-holder farmers, is primarily attributable to operator (enumerator) error (Fasbender and Lucau, 2012).

One concern with GPS measures in large scale surveys, which does not apply to self-reported measures, is the rate of missingness in the data. Missingness rates of 20-30 percent are not uncommon in existing datasets, and the pattern of missingness is not random but tends to be correlated with both plot and respondent characteristics. Kilic et al. (2013) show, with national data for Uganda and Tanzania, how plot distance from the interviewed household is the main factor determining what plots get measured, as field protocols normally include a provision not to measure plots beyond a given distance. Partly due to that, the plots that are not measured also differ systematically from those for which a GPS measure is taken in a number of (self-reported) characteristics, such as self-reported plot size, level of input use, and titling. Furthermore, some respondent characteristics are associated with higher missingness rates: plots belonging to older, less educated, poorer household heads, owning fewer plots are more likely to be measured than other plots. This is a drawback of GPS data that raises concerns about possible biases introduced by relying on observed GPS plot measures alone.

In presence of rates of missingness this high, imputations are
productivity gains achieved via a combination of policies and technological improvements. In particular, policies to boost energy efficiency have played a key role. These policies include the introduction of equipment, vehicle, and appliance efficiency standards, utility management programs, and building codes. The steep decline in U.S. oil intensity during the 1980s is typically attributed to energy conservation policies introduced from the mid-1970s onwards, following the 1970s oil price shocks. Moreover, the development of more energy efficient products and services has also contributed to a decline in oil use. However, improvements in energy efficiency have slowed down in recent years. In particular, the International Energy Agency (IEA) reports a decline in energy efficiency gains since 2015 and attributes it to a stagnation in the passing of new energy efficiency policies (see International Energy Association, 2017, 2019).

# **IV. Empirical Analysis**

## **IV.1 Data**

I use quarterly data covering the period 1980:Q1 to 2019:Q4. The database consists of real GDP growth rates, the real price of oil, U.S. and non-U.S. crude oil production, and U.S. per capita oil consumption. The data on real GDP growth rates are from the Global VAR (GVAR) dataset, 2019 Vintage (Mohaddes & Raissi, 2018). The GVAR dataset is based on Haver Analytics, the International Monetary Fund’s International Financial Statistics (IFS) database, and Bloomberg. The dataset consists of 33 countries, which include oil-importing advanced economies, oil-importing emerging markets and developing countries, and oil exporters. The oil market data consist of monthly series on U.S. and non-U.S. crude oil production, the real price of oil based on the U.S. refiner acquisition cost of imported crude oil, and U.S. per capita oil consumption. The oil market data is obtained from the U.S. Energy Information Administration (EIA). The U.S
Finally, the DCGE model links to a micro-simulation model, which allows for the endogenous estimation of growth impact on poverty reduction. All HBS sample households are included in the micro-simulation model, and their total expenditures and expenditures on each commodity or commodity group are linked to each of the six representative households included in the DCGE model. The endogenous changes derived from the DCGE model for the six representative households are used to recalculate consumption expenditure of their corresponding households in the survey dataset. New levels of total consumption expenditures are recalculated based on individual household budgets; and the new poverty rates for each region, rural and urban, and the national total are obtained by comparing expenditure levels (in real terms) with the official poverty line defined for HBS.

Table 6. Scenario Assumptions

||**Government transfers**|**Subsidy**|**TFP**|
|---|---|---|---|
|**Reform 1 (accelerated)**||||
|1A: Use all savings for budget<br>consolidation|as base|100%<br>decrease of<br>subsidy in<br>2011|as base|
|1B: Reduce fiscal deficit by<br>50% and use remainder for<br>direct transfers to households|increase transfers in<br>2011 between 40% and<br>380% depending on<br>initial size of transfers<br>and population shares|100%<br>decrease of<br>subsidy in<br>2011|as base|
|1C: Reduce fiscal deficit by<br>50%, compensate only the<br>poorest of the poor, and use<br>remainder for productivity-<br>enhancing investments|increase transfers in<br>2011 between 22% and<br>155% depending on<br>initial size of transfers<br>and population shares|100%<
work we are not discussing this further. Nonetheless, there is an abundant literature on the role of governance on the effectiveness of public expenditure.

29 There are many other examples of this type of complementary documented in the literature. For example, Deininger and Okidi (2003) use micro-level survey and panel-data evidence from Uganda spanning 1992-2000 and find that the benefits of education and health care for growth and poverty reduction depend on complementary investments in electricity and other infrastructure, and reductions in civil strife.

17
To represent business cycles, we use per capita real income growth in percentages, with a mean value of 1.96%. The impact of state ownership on the procyclicality of banks lending possibly depends on the general effectiveness of the government bureaucracy, i.e. good governance. As a proxy of this, we use a composite indicator of the government effectiveness from the World Governance Indicators (WGI) database (Kaufmann et al., 2010), which includes perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures. This variable is reported on an annual basis only after 2001, limiting regressions that include this variable to the period 2002-2010.

We consider several bank-level control variables, all lagged one year. Among these, assets are the log of total assets in constant 2000 dollars to capture the impact of a bank‟s toobig-to-fail status. Equity is the ratio of equity to total assets, to control for bank soundness, averaging 10.6% in our sample. Cooperative bank, real estate and mortgage bank and savings banks are dummy variables that are one if a bank is in the pertinent bank category. The loans over assets variable represents the relative importance of lending in a bank‟s activities, with a mean of 0.540. Liquidity, constructed as the ratio of liquid assets to total assets, can be a measure of bank soundness and its ability to sustain its lending, as well as an indicator of inefficiency (since too much liquidity comes at the cost of bank intermediation). In addition, deposits over total liabilities is a measure of the stability of a bank‟s funding, since non-deposit funding tends to flee quickly during periods of instability. In our sample the bulk of the funding source consists of deposits, with a share of 0.771.

We consider two macroeconomic controls from the World Development Indicators (WDI, 2011) database. These are GDP per capita in thousands of constant 2000 dollars, and
possible. This facilitates causal investigations into variation in crop losses or the microlevel impacts of disasters (Hallegatte and Rozenberg, 2017; Markhvida _et al._ , 2020).

Importantly, this paper does not study nor does it propose the use of survey data as a substitute for the other approaches presented. Rather, it highlights how survey data may yield additional insights and ultimately contribute to the development of more accurate, integrated approaches suitable for the context of LMICs. For example, high-quality survey data may be useful to train remote-sensing algorithms (Jain _et al._ , 2016) and survey data, geospatial data, and/or crop model data can be combined in statistical models to improve accuracy and analyze causal relationships (Imran, Zurita‐Milla and Stein, 2013; Hill and Porter, 2017).

Next to the issue of determining the magnitude of crop losses (quantification), accurately attributing losses to disasters is an essential challenge for this paper to study. As the most common disasters in our study region are rainfall-related (FAO, 2018, 2021), this task poses some challenges akin to those covered in the literature on agricultural index insurance (Miranda and Farrin, 2012; Cole _et al._ , 2013; Karlan _et al._ , 2014; Tadesse, Shiferaw and Erenstein, 2015).

Self-reported insurance claims may be hard to verify without significant administrative effort and may thus be subject to moral hazard and adverse selection (Tadesse, Shiferaw and Erenstein, 2015). To overcome issues of information asymmetry and reduce administrative costs, insurance contracts that tie payouts to a local index proxying for disaster impact intensity, e.g. a rainfall gauge placed at a representative location for a small geographic area, have received considerable attention in the literature. However, while “measuring” shock exposure through an index provides an objective proxy, it leaves farmers with a certain “basis risk”, the possibility that the index does not capture idiosyncratic deviations at the farm or even plot level.

Basis risk is
# Does Debt Relief Improve Child Health? Evidence from Cross-Country Micro Data<sup>_∗_</sup>

Anna Welander<sup>_†_</sup>

## **JEL Classification:** F34, I15, I18

**Keywords:** Debt Relief, Heavily Indebted Poor Countries Initiative, Child Health, Demographic and Health Surveys

> _∗_ I thank Therese Nilsson and Carl Hampus Lyttkens for invaluable support and comments during this research project. Comments and suggestions by Douglas Almond, Claes Ek, David Evans, Pontus Hansson, Petter Lundborg, Kaveh Majlesi, Wei Si, David Slusky, Mark Thomas, and seminar and conference participants at the World Bank, Lund University, the Nordic Health Economists’ Study Group Meeting 2015, the Public Choice Society Meetings 2016, the Nordic Conference in Development Economics 2016, and the Swedish Conference in Economics 2016 are gratefully acknowledged. I also thank David Evans and the World Bank’s Office of the Chief Economist in the Africa Region for welcoming me as a visiting fellow and the Jan Wallander and Tom Hedelius Foundation and the Swedish Research Council for Health, Working Life, and Welfare, the Health Economic Program at Lund University for financial support. All remaining errors are my own.

> _†_ Department of Economics, Lund University School of Economics and Management, E-mail: anna.welander@nek.lu.se
be. However, the approach comes with limitations. First, it does not provide the user with the information on whether the variables included are associated in a statistically significant way with the success outcome of interest (i.e., attracted FDI or a revealed comparative advantage in a sector). Yet, the ability to understand which variables, among many variables, are the statistically significant (key) binding constraints is of high importance for efficient decision making (both from the investor and policy maker perspective). Second, the benchmarking process (i.e., assigning scores from 1-5 to each indicator for every country/sector pair) considers each indicator in isolation and is based only on the past information about the values of the indicator and the sector-level success performance. The process does not account for the effects of other relevant indicators or of potential associated factors, thereby potentially producing biased estimates.

# Identification of factors associated with trade and investment in a high-dimensional framework

# Objectives

Our approach is based on complementing the work discussed above by addressing some of its limitations. In particular, the objectives of the analysis are the following:

- Reduce the complexity of the universe of indicators associated with trade and investment outcomes success at the sector level.

> 14 Accessible at https://tcdata360.worldbank.org/

> 15In an earlier version of the tool, IFC’s DOTS database has been used. The DOTS database records IFC investments (so not only foreign investments but also domestic ones to which IFC is part of) and their performance. Thus, the tool enabled to benchmark performance against that of profitable investments, offering a more robust benchmark.

8
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|0.1|
|Maputo City|381|41|0|0|313|35|0|0|
|Total|**1383**|**15**|**1068**|**12**|**1381**|**12**|**1064**|**9**|

**Set A:** Consumption poor/ Wealth index non-poor; **Set B:** Consumption non-poor/ Wealth index non-poor;

Regarding Set B, i.e. the consumption non-poor/wealth poor set, around 80 percent of observations are concentrated in Central and Northern rural areas in both surveys (see Table 1). The general

> 12 These are: the non-food share of household consumption, the head-of-household’s total years of education, the highest level of education attained by any household member, the household’s total asset ownership, access to a protected water source, electricity and sanitation, housing conditions and dietary variety.

7
The data used to construct the legal gender disparities measure are provided by the World Bank's _Women, Business and the Law_ database. Data are collected through several rounds of interaction with practitioners with expertise in family, labor and criminal law, including lawyers, judges, academics and members of civil society organizations working on gender issues. The data are constructed based on the responses to questionnaires, conference calls, written correspondence and visits by the _Women, Business and the Law_ team. The respondents also provide references to the relevant laws and regulations. The _Women, Business and the Law_ team collects the texts of relevant laws and regulations and checks questionnaire responses for accuracy. Questionnaire responses are verified against codified sources of national law including constitutions, codes, laws, statutes, rules, regulations and procedures, in areas such as labor, social security, civil procedure, tax, violence against women, marriage and family, inheritance, nationality and land. The users of _Women, Business and the Law_ data are of a wide variety. Institutions such as the World Bank, OECD, United Nations, IMF, and the Millennium Challenge Corporation have all used the data. Academic studies have also been conducted based primarily on _Women, Business and the Law_ data (Amin and Islam, 2015; Gonzales et al., 2015).

A number of composite measures of gender inequality do exist, and they typically try to capture gender inequality as a whole or different aspects of it. Several indices have been constructed to capture gender inequality in outcomes such as gender differences in education, employment, health and political involvement. These include three measures from the UNDP: Gender Inequality Index (UNDP, 2010), Gender Empowerment Measure and the Gender-Related Development Index (UNDP, 1995). Other measures also based on outcomes include the Global Gender Gap Index (Lopez-Claros and Zahidi, 2005), the Gender Equity Index (Social Watch, 4
in the Appendix show the balance test results for each sample. For Experiment 1, we found no statistically significant differences in covariates by treatment arm. For Experiment 2, we tested 15 mean differences (5 variables between 3 treatment groups). We found a lack of balance for men and women in one variable (tertiary education), within the tolerance level of 10%. All variables are included in the regression as controls to account for potential imbalances.

# _3.5. Descriptive statistics_

This section presents descriptive statistics from the impact variables collected in the post-intervention survey. Figure 2 shows responses on recalling receiving the message by experiment. On average, 38.5% of

> 10 We consider individuals who applied for the benefit between September 1, 2021, and June 6, 2022.

9
**Table 10 R&D Expenditures in  2001**

||**_Million $_**|**_$ per Employee in_**<br>**_R&D_**|
|---|---|---|
||_2001 _|_2001 _|
|FordMotorCo, USA|7,400|20,879|
|General Motors Corp., USA|6200|16,986|
|SiemensAG, Germany|6,028|12,455|
|Ericsson, Sweden|4,516|53,002|
|Finland|4,422.6|84,073|
|Denmark|3,604.2|101,100|
|**Latvia**|**81**|**8,719**|

_Source_ : OECD, 2002 ; Eurostat 2003 ; calculations by the authors

- Government R&D policy papers refer to five priority areas – ICT, electronics, material sciences, pharmaceutical/ biotechnology, and wood chemistry.  But actual government spending bears no relationship to stated government priorities. Instead, limited government financial resources are spread over 14 areas.  These 14 research fields were defined in the early 1990s and have not been revised since.  Moreover, for the past ten years, government R&D spending has been allocated in the same proportions among these 14 priority areas.  Scientists determine areas of research interest based on their scientific interests.  This bears only an accidental relationship, at best, to the needs of the economy in general or individual enterprises in particular.  This system is basically a social safety net for scientists.  But what began as a coping mechanism to keep the old R&D system intact at a time of wrenching economic change has become an obstacle to progress.

- As is well known in Latvia, there is very little private spending for R&D.  Indeed, Latvia ranks below all NMS’s in terms of the proportion of total R
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This approach to constructing a counterfactual never requires that households have access to additional cash in order to buy in bulk (although they might need the cash a little sooner, once in the lifetime of the household—see the extensive analysis in the first subection of Section 5). Because we use observed total two-week quantities in constructing the counterfactual—not large, hypothetical purchases off of the observed support—buying in bulk as defined here can only increase, not decrease, the household’s cash reserves over the total two-week period.

Because we are estimating price schedules, not demand curves, the approach is not threatened by censoring concerns related to the two-week observation window. The most likely form of data censoring is that very large purchases, e.g., of wholesale bags of grain, are too infrequent to appear as focal quantities, even though they may be widely available at markets. The absence of these large-quantity purchasing opportunities means that our calculations are likely to be lower bounds on actual losses.

# **3 Data and descriptive patterns**

The data for this paper are from the Survey of Household Welfare and Labor in Tanzania (SHWALITA). The survey was part of an experiment to test the impact of questionnaire design on consumption measures (see Beegle et al. (2012) for details). In one arm of the study, 9 households per village were randomly assigned to complete a consumption diary. Three of these households completed a single, household-level diary, with no monitoring by project staff. Three completed a single household diary but received multiple follow-up visits from field staff. For the last three households each adult member kept their own diary, with children placed on the diary of the most knowledgeable adult. Households in the third group received multiple follow-up visits, similar to those in the second group. The differences between module arms are small but not zero, and they have no impact on the findings in this paper. We control for diary type whenever relevant.

The SHWALITA survey was conducted in
The poverty rate at US $ 1.90

- 2) GDP per capita, ppp adjusted

- 3) The 3-year average real GDP growth rate

- 4) The 3-year average population growth rate

- 5) The 3-year average CPI (Consumer Price Index) inflation rate

- 6) Agriculture, forestry, and fishing, value added (% of GDP)

- 7) Food imports (% of merchandise imports)

- 8) Fuel imports (% of merchandise imports)

- 9) Agricultural land cover (% of land)

- 10) Forest land cover (% of land)

- 11) Historical child mortality (1995-2015 average rate)

All the data, including the dependent variable, are taken from the WDI (accessed by API on September 1, 2022). All the data comes from standard sources