rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 9676

# **Abstract**

Several developing countries are currently implementing phone surveys in response to immediate data needs to monitor the socioeconomic impact of COVID-19. However, phone surveys are often subject to coverage and non-response bias that can compromise the representativeness of the sample and the external validity of the estimates obtained from the survey. Using data from high-frequency phone surveys in Ethiopia, Malawi, Nigeria, and Uganda, this study investigates the magnitude and source of biases present in these four surveys and explores the effectiveness of techniques applied to reduce bias. Varying levels of coverage and non-response bias are found in all four countries. The successfully contacted samples in these four countries were biased toward wealthier households with higher living standards. Left unaddressed, this bias would result in biased estimates from the interviewed sample that do not fully reflect the situation of poorer households in the country. However, phone survey biases can be substantially reduced by applying survey weight adjustments using information from the representative survey from which the sample is drawn. Applying these methods to the four surveys resulted in a substantial reduction in bias, although the bias was not fully eradicated. This highlights one of the potential advantages of drawing phone survey samples from existing face-to-face, representative surveys over random digit dialing or using lists from telecom providers where such adjustment methods can be more limited.

This paper is a product of the Development Research 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 kmcgee@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 quality-adjustment factor in our study is calculated in the following way: where _m_ corresponds to the productivity of a tertiary degree coming from a “zero-quality” institution; _Q_ corresponds to the average quality score of universities in country _c_ , ranging from 0 to 100; _β_ is a productivity-adjustment factor that transforms the quality score into productivity units; and _m_ is scaled in a way that quality adjustment ( _QAc_ ) equals 1 if _Qc_ equals 100.

The measure of quality corresponds to the information collected by global university rankings. These rankings, published by private, for-profit companies, have grown in number over the years. They are usually based on an underlying score that is usually a weighted average of scores on different aspects of higher education (the volume and quality of research, research influence, the quality of teaching, international outlook, links to industry). These rankings do not include all higher education institutions (universities need to send their information, usually at a cost, to the publishers), and they use different methodologies. Our analysis relies on a combination of several of these ranking, including the scores from the Times Higher Education (THE) ranking; the Quacquarelly Symonds (QS) ranking; the Academic Ranking of World Universities (ARWU, also known as the “Shanghai” ranking); the Center for World University Rankings (CWUR); the U.S. News Global Universities Ranking; and the U-Multirank ranking (a nonnumeric, userdefined ranking). These rankings contain information on 400–1,000 universities in 43 countries in Europe and Central Asia. We generate a country-level average by averaging the scores for all the universities in a given country included in each ranking, yielding six values for each country (one for each ranking source). As detailed later, we normalize each of them, and then take the average of them as the aggregate quality score.

# _University rankings_

Table 1 describes the six university rankings used in this analysis. The CWUR includes the largest number of universities (2,
- <mark>You, J. (2014). “Dietary change, nutrient transition and food security in fast-growing China.” in R. Jha, R. Gaiha and A.B. Deolalika, eds., Handbook on Food, pp.204-245. Cheltenham, UK, Edward Elgar Publishing.</mark>

<mark>Zezza, Alberto, Carletto, Calogero, Fiedler, John L., Gennari, Pietro, Jolliffe, Dean. (2017)</mark>

- <mark>“Food counts. Measuring food consumption and expenditures in household consumption and expenditure surveys (HCES).” Introduction to the special issue,</mark> _<mark>Food Policy</mark>_ <mark>, Volume 72: 1-6.</mark>

- <mark>Zwane, Alix Peterson, et al. (2011). "Being surveyed can change later behavior and related parameter estimates."</mark> _<mark>Proceedings of the National Academy of Sciences</mark>_ <mark>108.5: 1821-1826.</mark>

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five years have been used to pay current retirees, as the government has not made a clear decision on how to finance the transition cost.

- X _The funding problem is acute._ Many local pension pools are financially bankrupt as compliance rates are low and declining and the issue of how to finance the transition cost is unresolved. According to MOLSS statistics, state enterprises owe the government RMB 38 billion in pension arrears. Local governments have been using the reserves in pillar 2 to pay pensions to current retirees, leading to notional or empty individual accounts. And where funding has still been inadequate, local tax revenue has been used. The Ministry of Finance data indicates, in 1999, the MOF also transferred RMB 17 billion to 25 provinces to cover pension shortfalls in localities. The central government is in effect bailing out local pension pools, many of which are bankrupt. This trend, if unchecked, will become a threat to the fiscal sustainability of the central government.

# **Implicit Pension Debt and Transition Issues**

_Implicit pension debt (IPD)_ refers to the benefit promises a pension scheme makes to workers and pensioners and is measured by adding the present value of benefits that have to be paid to current pensioners plus the present value of pension rights that current workers have already earned and would have to be paid if the system were terminated today. IPD usually is calculated under the termination hypothesis that the unfunded system is to be terminated immediately and that all pensioners and workers must be compensated for their future pensions and accrued rights. IPD, a stock concept, represents the direct and implicit liabilities that a government can predict with certainty and must include in its fiscal plans. Unlike a government's contingent liabilities (such as bailing out state banks), IPD poses no unexpected demand for fiscal expenditure.

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- **Eligible non-beneficiaries** (households with at least one vulnerable individual but not benefitting from any VG sub-projects),

- **VG Beneficiaries** (households with at least one vulnerable individual and benefitting from a VG sub-project). This group was further stratified into two groups:

- **VG group leaders** (chairperson, secretary, and treasurer, who hold signatory power over group accounts), and

- **VG “rank and file”** members (the rest of the group conducting the proposed income generating activity – with no signatory powers over the group accounts)

Within each village, a short listing survey was given to every household. The short listing survey collected basic demographic information about the household (e.g. household size and age of the eldest household members), GPS data, and determined whether or not the household contained a vulnerable member. A long listing survey was given to all village elites, all households with vulnerable members (including VG beneficiaries and eligible non-beneficiaries – 38,871 households in total), and to a randomly selected sample of ineligible households. The long listing survey collected more detailed data, including household amenities, characteristics of the household head, holdings of assets, and basic consumption data. From within this sample, a smaller, random stratified (using the strata described above) sub-sample was selected to receive a detailed household survey.

In each of the 100 villages, a household survey was conducted with the two village elites, the three group leaders from each TASAF II group, three randomly sampled “rank and file” members from each TASAF II group, three randomly sampled households from all “eligible nonbeneficiaries” in each village, and three randomly selected households from all “ineligible” households in each village. Hence, in a typical village with one TASAF II group funded to run an income generating activity, the sample size was 14. There were 1,544 households that completed the household survey in the 100 villages. The household
# **2.1 Corruption**

One definition of corruption could be “[t]he abuse of entrusted authority for illicit gain” (Norwegian Agency for Development Cooperation 2008). This could include both subjective and objective measures. For instance, corruption might range from bureaucratic corruption to a broader notion that further includes nepotism, patronage networks, fraud, conflicts of interest, bribery, embezzlement, extortion, favoritism, and political corruption (Johnston 2005). Given the complexity of capturing this variable, there exists a lot of debate on how to appropriately measure it.

In this paper, we try to capture the different elements of corruption by considering more than one measure. In particular, the dependent variable is based on three subjective indicators of corruption: _i_ ) the Corruption Perception Index (CPI); _ii_ ) the Control of Corruption Governance Metric (WGI CC); and _iii_ ) the Government Effectiveness Governance Metric (WGI GE). We also consider another measure of corruption: the International Country Risk Guide (ICRG). As shown in Hamilton and Hammer (2018), the survey-based indicators (in particular, the CPI and the WGI CC indicator) are the most valid measures of overall corruption in many country contexts. However, and in line with the existing literature on corruption, Hamilton and Hammer (2018) suggest to cross-check the results when using one measure of corruption with the use of additional indicators, as significant differences exist in the construction of these measures.<sup>5</sup> So far, no consensus on the optimal measure of corruption has been reached.

The CPI, an annual index published by Transparency International since 1995, defines corruption as the misuse of “entrusted power for private benefit” (Transparency International, 2011). It ranks countries by the perceived levels of public sector corruption, as determined by expert assessments and opinion surveys.<sup>6</sup> In particular, the CPI is a composite index based on a combination of surveys and
200 kilometers away. For the case of Tanzanian firms covered by this survey there is no detectable significant relationship between disaster risk perception or actual flood experience and distance to the average supplier, when employing linear regressions (table D.3, appendix D). Instead, differences in supply network extents across firms can mainly be explained through region and sector fixed effects, with firms in Dodoma and Tanga maintaining on average more spread out supply networks than those in Dar es Salaam.

# 5.3. Investing in backup capacity can ensure business continuity but also results in risks

By investing in water tanks and electricity generators, firms can ensure business continuity even when infrastructure systems are disrupted by disasters (Hallegatte et al. 2019). The survey data show that about 64 percent of the firms that depend on water for their production own a water tank, and 30 percent of firms own power generators. The evidence presented in this section shows that water tank and generator ownership are strongly driven by economic factors, such as the local price of water and electricity or firms’ usage levels. There is some evidence that experiencing a flood on business premises increases the probability of water tank ownership, but decreases the probability of owning a power generator.

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across all exporting countries and years to implement the above specification, by estimating the average _ann_ and _cnm_ for all exporting countries. Specifically, let each _n_ represent an HS 6-digit product, and _n_<sup>_′_</sup> represent the same HS 6-digit product from an exporting country. Then each country’s unique _an′n′_ is _ann_ plus a mean-zero error term, _κ_<sup>_t_</sup> 2 _n_<sup>_′_.Likewise,</sup><sup>_cn′m_is</sup> _cnn_ plus a mean-zero error term, _κ_<sup>_t_</sup> 3 _n_<sup>_′_:17</sup> The above specification is possible because the error terms _κ_<sup>_t_</sup> 2 _n_<sup>_′_and</sup><sup>_κt_</sup> 3 _n_<sup>_′_arenotcorrelated</sup> with relative prices and endowments.

# **3 Data**

The analysis of this paper uses HS 6-digit panel data sets which consist of the EU’s MFN ad valorem tariffs and trade with its 100 top trading partners for the period between 2010 and 2017. Using panel data allows elasticities estimates to be based on variation both across countries and across time. One advantage of using panel data is that it allows us to obtain estimates in cases where there is insufficient tariffs’ variation in the cross section setup or for HS-6 digit products with no consistent trade throughout the sample period.

Trade and tariff information originate from the World Bank World Integrated Trade Solution (WITS). The World Bank’s World Development Indicators provides the data on endowments and GDP. Table 1 presents some summary statistics of the EU trade policies with respect to products from the UK, under the assumption of MFN ad valorem tariffs

> 17 The error terms are mean-zero because we are estimating the average _ann_ for each product _n_ .

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3), and (2) assumptions about the incidence of taxation (columns A-B-C in Table 3). In all the scenarios VAT burden is assumed to be shifted to consumers, the non-take up ratio of means-tested benefits and unreported incomes are assumed to be constant. Under each scenario of this static micro-simulation we tested several options of the degree of formalization by hypothesizing transitions of varying percentages of informal employees into formality: from 0 percent (which implies that the tax maneuver has no impact on formalization), to 10 percent, 20 percent and 50 percent<sup>17</sup> of all informal workers (i.e., full formalization). The individuals undergoing transitions have been selected randomly from the pool of informal workers.

> 15 This definition of informality differs from the definition used by Rosstat. The official Rosstat definition of informal employment is estimated using the Labor Force Survey (LFS) and includes workers that are employed but not at an enterprise in their primary or secondary job. No reference is made to envelop payments. As of 2016, the informality rate according to Rosstat is 21.2 percent.

> 16 Given the definition of informality of this exercise, turning formal means the worker has no earnings that do not pay SIC and PIT. Namely, if he/she had a share of earnings as envelope payments or had no contract, becoming formal means having a work contract and no envelop payments, so 100% of earnings are subject to SIC and PIT. The simulation exercise excludes the self-employed workers because the tax maneuver would not affect the taxation mechanism for this group. 17 Formalization of 30%, 40% and 100% of informal workers were estimated too. The four abovementioned were selected to demonstrate for the following reasons: 0%, 10% and 20% are the most realistic cases, that one should expect as reaction for tax maneuver (based on probabilistic simulations described further) and 50% leads to a fiscally neutral effect under Scenario 2.

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Table 1 (cont.) Household Surveys

|Country|Year|Obs|Survey|
|---|---|---|---|
|Armenia|2014|5124|Integrated Living Conditions Survey|
|Bangladesh|2010|12117|Household Income and Expenditure Survey|
|Bhutan|2012|8879|Living Standards Survey|
|Cambodia|2013|3801|Socio-Economic Survey|
|Indonesia|2007|12876|Indonesian Family Life Survey|
|Iraq|2012|24895|Household Socio-Economic Survey|
|Jordan|2010|11110|Household Expenditure and Income Survey|
|Kyrgyz Republic|2012|4962|Intergrated Sample Household Budget and Labor Survey|
|Mongolia|2011|11089|Household Socio-Economic Survey|
|Nepal|2010-2011|5929|Living Standards Survey|
|Pakistan|2010-2011|16178|Social and Living Standards Measurement Survey|
|Papua New Guinea|2009|3776|Household Income and Expenditure Survey|
|Sri Lanka|2012-2013|20335|Household Income and Expenditure Survey|
|Tajikistan|2009|1488|Tajikistan Panel Survey|
|Uzbekistan|2003|9419|Household Budget Survey|
|Vietnam|2012|9306|Household Living Standard Survey|
|Yemen, Republic of|2005-2006|12998|Household Budget Survey|
|Azerbaijan|2005|4797|Household Budget Survey|
|Georgia|2014|10959|Household Integrated Survey|
|Moldova|2014|4836|Household Budget Survey|
|Ukraine|2012|10394|Sampling Survey of the Conditions of Life of Ukraine’s Households|
|Bolivia|2008|3900|Encuesta de Hogares|
|Ecuador|2013-2014|28680|Encuesta de Condiciones de Vida|
|Guatemala|2014|11420|Encuesta Nacional de Condiciones de Vida|
|Nicaragua|2009|6450|Nicaragua - Encuesta Nacional de Hogares sobre Medici ́on de Niveles de Vida|

Notes: List of Household Surveys in the HIT database.

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|(b)|(c)|(d)|
|Lerner index|-2.758***<br>[0.568]|-3.219***<br>[0.476]|||
|Concentration 3|||-0.333<br>||
||||[0.341]||
|Herfindahl index||||-0.729*|
|||||[0.424]|
|LernerCredit information|0.557***|0.642***|||
||[0.132]|[0.104]|||
|Concentration 3Credit information|||0.059<br>[0.076]||
|H IndexCredit information||||0.184*<br>[0.101]|
|Log firm size|0.087***|0.088***|0.089***|0.089***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.019**|0.015*|0.024**|0.025**|
||[0.009]|[0.009]|[0.009]|[0.010]|
|Exporter|0.033***|0.031***|0.031***|0.031***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign owned|-0.080***|-0.092***|-0.084***|-0.082
This will make it difficult to interpret IRFs of the identified shock in question, given they will represent a combination of drivers with only an _a priori_ understanding of their relative weights.

# **8 Conclusion**

In summary, this paper documents the biases that can be introduced into the longrun and increasingly widely used Max-Share SVAR identification methodologies by the presence of confounding shocks. We show theoretically why this is the case, and why Monte Carlo DSGE simulations have previously failed to account for this issue given the overwhelming dominance of technology shocks in such models. Three new SVAR identifications are proposed to deal with confounding shocks with different frequency domain properties.

Using a simple two-variable model, we show that our new NAMS approach is less susceptible to confounding low-frequency shocks when trying to identify highly persistent technology shocks. And we propose two types of SVAR identifications in the frequency domain which show significantly reduced estimation bias in the presence of higher-frequency confounding shocks relative to technology. We demonstrate that the US productivity data is likely to be affected by confounding shocks that affect the level of productivity temporarily and that US productivity is likely to be a unit root process driven by stochastic technology growth shocks. We propose that the Spectral identifications are the most robust to the features of the data, although the NAMS approach has an advantage in being less affected by the confounding transitory (low-frequency) shocks to productivity. Finally, we show that similar issues affect EMDE data, which is often more volatile than advanced economy data.

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large firms in the United States is likely to be a relatively pure measure of their demand for external finance.” In the same spirit, we exploit the US IO tables to better identify a production process that is less subject to distortions (such as product market restrictions), and therefore can be considered among the most efficient, allowing the identification of a production process driven by technological reasons.

Furthermore, we exploit the index of restrictiveness of EU-RoO developed by Cadot et al. (2005). This RoO index applies to final goods classified at the HS6 level, and varies from 1 to 7, with 7 representing the most restrictive EU-RoO. This index is based on change in tariff classification (CTC) and almost always accompanied by other criteria to be met to obtain origin. As a matter of comparison, less than 17% of EU-RoO are uniquely based on CTC, against 89% of NAFTA RoO. Additionally, EU-RoO are largely based on value added requirements (VA). In fact, 26% of tariff lines are based on CTC and VA requirements, 13% of which uniquely rely on VA criteria.<sup>5</sup> This seems to suggest that the EU-RoO are more heterogeneous than the NAFTA RoO. To account for the heterogeneous level of complexity embodied in the EU-RoO, the synthetic RoO index constructed by Cadot et al. (2005) seems more appropriate than counting the number of final goods products that use a specific intermediate.

To translate the EU-RoO for final goods in terms of intermediates, we combine the previous

> 5For further details see Table 2 in Cadot et al. (2005).

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were at least 2 kilometers away from the study or any other paved road. About 50 households were selected from each study village using a stratified random sampling procedure. Both surveys collected a variety of information on general household characteristics, education, healthcare treatment, wage and self-employment, credit activities, assets, income, consumption, marriage, and fertility. Additionally, information on community characteristics and transportation was also collected.<sup>9</sup> The outcomes of interest include variables such as household transport expenses, fertilizer price, male agricultural wage, agricultural output and price indices, and household outcomes such as per capita expenditure, male and female labor supply, and boys’ and girls’ schooling.

Road improvements affect the household through changes in three mechanisms: (1) transportation costs as well as input and output prices; (2) labor supply, as well as farm and non-farm production; and (3) household outcomes such as earnings, consumption, and schooling. In our results, we first examine the impact of road development on transport costs. Since there were very few households that reported transportation costs for production, we therefore focus on household transport costs that include costs incurred while going to such places as the market center, school, and nearest health facility.

Next, we consider input and output prices such as fertilizer (urea) price, daily agricultural wage for men, as well as agricultural output and price indices.<sup>10</sup> For the latter indices, we use the Laspeyres quantity and price indices for agricultural production. In constructing the Laspeyres indices, let _k_ stand for commodity, _i_ for household, and _t_ ={0,1} for year before and after the project. Defining the base year ( _t_ =0) price for each commodity as<sup>_P_</sup> _k_ 0<sup>, and the base year quantity of each commodity produced by the</sup> <u>∑</u> _k