rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1. Introduction

The outbreak of the COVID-19 virus has had far-ranging impacts across the globe. In addition to the serious health effects, COVID-19 has caused widespread economic disruption. The epidemic has resulted in closure of schools and markets, disruption in public services, banning of social gatherings and restriction of mobility and transportation of goods and people. Given these wide-ranging impacts of the pandemic, governments, policy makers and researchers are in dire need of just-in-time and reliable data both to monitor the health and socio-economic impact of the epidemic on livelihoods and well-being. However, amid a pandemic it is difficult to deploy enumerators to the field to collect information from households and communities through face-to-face (F2F) interviews given the risk of transmission to both interviewer and respondent. As a solution to this challenge and to address the urgent demand for rapid and informative data, high frequency phone surveys (HFPS) of households are being implemented in many developing countries. The improvement of phone penetration in these counties in recent years, as well as the existence of recently conducted representative household surveys that contain re-contact information for some or all household members, has created opportunities to conduct phone surveys.

While phone surveys have distinct advantages that are particularly relevant during the pandemic, they also suffer from drawbacks. One of the most substantial weaknesses of phone surveys is that they are often subject to several sources of bias that can compromise the representativeness of the sample and the external validity of the estimates obtained from the survey (Himelein et al, 2020). This study investigates the magnitude and source of bias in recent telephone surveys as well as exploring the effectiveness of techniques to reduce it. Using telephone surveys in Ethiopia, Malawi, Nigeria and Uganda that were sampled from recently completed representative F2F surveys, this study will first explore the sources of bias introduced at each stage of the phone survey selection and interview process, by comparing the profile of characteristics of the F2F and the phone survey samples. After examining the source and extent of bias, it will then assess the effectiveness of
The analysis proceeds by estimating equation (1) with the two Landsat indices above. Since these indices are already normalized and well bounded, there is no need to apply the natural logarithm. Table 2, Panel B summarizes the results. In column (1), both indices are statistically significant in explaining differences in the (log) number of enterprises between communes. While a higher measure of built-up areas is positively correlated, the coefficient on NDVI is negative and indicates that the presence of vegetation predicts fewer enterprise in the cross-section. This is not surprising as high NDBI and low NDVI values indicate the presence of cities. NDVI is often used as a measure of agricultural productivity, but as self-employed farmers are not covered by the enterprise survey, the vegetation measure does not reflect this. The R-squared measure of the model is 0.341, suggesting a rather high prediction power with the use of only two indices. This is about four times larger than in the nightlights model.

The coefficients in the regression on employment are largely similar, indicating that employment is higher in more urban places. The Landsat indices explain 25.7% of the variation in employment and thus about 2.5 times as much as the nightlight approach (10.3%). The coefficients in the expenditure model are much smaller in absolute value, but still highly significant at the 0.1% level. Again, the difference in predictive power of both remote sensing approaches is striking. The simple Landsat model explains about 10 times as much of the variation as the sum of light measure ( _R_<sup>2</sup> = 0 _._ 274 versus _R_<sup>2</sup> = 0 _._ 023). Figure 2 summarizes the predictive power of the models by plotting the actual values versus the predicted values for all three economic outcome variables.

When controlling for commune size, the coefficients on NDBI and NDVI are reduced in absolute value, but remain highly statistically significant. This indicates
**Figure 3: Employment growth and informal competition relationship (Median values)**

<!-- Start of picture text --> Figure 3A<br>0<br>Low informal competition High informal competition<br>-0.2<br>(residuals; roughly below (residuals; roughly above<br>-0.4 median) median)<br>-0.6<br>-0.8<br>-1<br>-0.94<br>-1.2<br>-1.4<br>-1.35<br>-1.6<br>Figure 3B<br>0<br>Low informal competition High informal competition<br>(residuals; roughly below 75th (residuals; roughly above 75th<br>-0.5 percentile values) percentile values)<br>-1<br>-0.91<br>-1.5<br>-2<br>-1.93<br>-2.5<br>Employment growth rate (%,<br>residuals, group median values)<br>Employment growth rate (%,<br>residuals, group median values)<br><!-- End of picture text --> Source: Author’s own calculations based on Enterprise Surveys data. Note: 1) Employment growth rate is calculated as the log difference in the level of employment (log of final year employment minus log of initial year employment) divided by the number of years between the initial and last year and multiplied by 100 to convert to percentage terms. The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against the residuals of informal competition (X-axis). The residuals are obtained by regressing the employment and informal competition (separately) on industry fixed effects, year fixed effects, initial level of employment
percentage points more likely to trust in the Salvadoran president.

In columns 5 and 6 of Table 3 I focus on trust in the municipal government. Average per capita family income at the municipality level is a strong predictor of trust in local government ( _p_ -value<0.01). Individuals in municipalities experiencing a one-standard deviation increase in the log of average per capita family income are 3.5 percentage points more likely to trust in this institution. When extending the sample and using NTL density as main explanatory variable I find again a slightly larger standardized effect (albeit statistically weaker): a one-standard deviation increase in NTL density is associated with a 4.5 percentage points increase in the likelihood of trusting the municipal government. The results in columns 5 and 6 of Table 3 also shed light on other determinants of institutional trust: individuals living in rural areas tend to trust more whereas men, older and more educated individuals tend to trust less in local government. (Generally, more educated individuals tend to trust less regardless of the institutions.) Interestingly, the size of central government transfers (FODES) positively correlates with trust in municipal government. The magnitude of the latter effect is economically significant: a one-standard deviation increase in per capita central government transfer (i.e., USD 38.2 per inhabitant) is associated with a 7 percentage points increase in the likelihood of trusting the municipal government.

> 13Using data from the World Value Surveys for 79 countries Algan and Cahuc (2013) show that age positively correlates with generalized trust across while gender does not predict trust.

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pre-break. In fact the identified break was in this instance related to the EMS crisis of 1992-93 rather than to liberalization.)

> 24 Liberalization is not irreversible; the data from Malaysia (late 1981), and to a lessor extent India (early 1984) and Pakistan (late 1985) provides some indications of a reversal to a narrower range of fluctuation. 25 Appendix Table A gives the dates from these studies for additional developing countries.

15
The second type of error involves discrepancies between recorded food expenditures and actual food quantities consumed. A household may correctly report its expenditures on an item, but may not be able to accurately report the specific quantity of that item, particularly given the indefinite measures (heaps, sacks, etc.) used by the survey. Such an error would clearly affect the unitary price of the item.

In order to compare the relative error sensitivity of PLs for urban and rural areas, we constructed a single national-basket PL, as well as national-level urban and rural PLs (not reported), the latter deflated with a location-based Paasche index, and then compared these with the location-specific PLs used to determine the official poverty statistics. It is important to recall that “location” in this context means each of the 13 regions listed in Table 1, a division of the country that groups homogenous urban and rural areas, sometimes from different provinces, into the same region. Our hypothesis is that locationbased PLs are more strongly affected by measurement errors in consumption data than are the overall national and national-level urban and rural PLs. Sensitivity tests on consumption data classified as unreliable (Set A and Set B observations, described above) have been constructed. Two tests are reported: (i) a test of the non-food component of the PLs; and (ii) a test of the final (food and non-food) PLs.

Table 2 illustrates our first sensitivity test of the non-food component of the PLs, constructed exactly like those in the three NPAs.<sup>27</sup> Column 4 of Table 2 shows, for each of the 13 regions, the non-food share of observations used to calculate the non-food PLs. The amount that these households spend on non-food items becomes the non-food poverty line; their share of non-food consumption is the value reported. As noted above, the non-food PLs rely heavily on observations in which the share of non-food consumption is very large due to the underestimation of food consumption.<sup>28</
(Cameron and Trivedi, 2005). To account for these aspects of the sampling design and obtain nationally representative estimates, I weight each sample observation (round-household/roundindividual) by the inverse of its probability of inclusion in the sample in the ESS2. The main parameter of interest is . It captures the average impact of refugee intensity on the welfare of host households. To identify the impact, I leverage a large spatial difference in ββ within-village temporal variations in refugee intensity, i.e., the intensity of the net influx of refugees, accounting for household- and round-specific fixed effects and a rich set of time-varying covariates.

**4.2. Identification** The estimates of the main coefficient of interest will have causal interpretation if the concerns of reverse causality and omitted variable bias are properly addressed. To assuage the former (ββ) concern, the index capturing refugee intensity is time-lagged relative to the period over which the outcome variables are measured. Omitted variable bias could stem from two decisions: the decision to leave refugees’ home countries (selection into migration to Ethiopia) and the decision relating to where to settle in Ethiopia (selection into places of settlement). To the extent any of these decisions involve refugees’ self-selection may lead to a bias in the estimates. Self-selection into migration is less important in the present case as refugees are forced migrants suddenly fleeing mainly conflict and violence, and small others political persecution (mandatory national service such as military conscription, gross human rights violation, detention, and others) and natural calamities.

The overwhelming majority of refugees in Ethiopia are hosted in formal camps or semiformal settlement sites (transit centers and entry points), in the latter case only until they are relocated to formal camps, which are located close to the country’s borders with the neighboring countries whence the majority of the refugees come (Eritrea, Somalia, South Sudan, and Sudan). The government decides on the location of these settlement sites<sup>25</sup> and
In a hypothetical scenario fashioned after the current pandemic period – where firms have no revenues due to a lockdown or collapsed demand – the median firm has retained earnings and other sources of financing to last 8 (in retail) to 19 weeks (in other manufacturing), while the average firm does so within 12 (in construction) to 38 weeks (in manufacturing of chemical, plastic and mineral products).

Across countries, the median Ukrainian firm is the most liquidity constrained, while the median Peruvian firm has the most breathing space. The former has 7 weeks buffer in retained earnings and other sources of financing, the latter 16 weeks. The same is true when we turn to the analysis of means, the average Ukrainian firm has a 10-week buffer in retained earnings and other sources of financing, while the average Peruvian firm has 27 weeks.

Our analysis does not support the Schumpeterian view that economic crises cleanse the private sector from inefficient firms. The evidence suggests that firms suffer untimely illiquidity regardless of age, size and productivity levels. In particular, survival times are not associated with higher firm productivity, with the exceptions of firms in Greece, Kenya and Peru. Larger firms have longer survival times only in Kazakhstan. Older firms are more resilient only in Jordan and Morocco.

The results here complement recent work by Bachas and Brockmeyer (2020), who use corporate tax records to show that even small revenue shocks push the majority of firms in developing economies into loss-making territory.

Section II describes the data, section III outlines the methodology, section IV illustrates the base results, section V delineates the hypotheses used, section VI presents regression results and Section VII details initial policy measures. Section VIII concludes.

# **II. Data**

The calculations use data for 11,759 businesses from the World Bank’s Enterprise Surveys conducted in a dozen economies across Africa, Central Asia, Europe, Latin America, and the

> and low-income economies where the informal sector constitutes a large share of economic activity. In
period or only with great difficulty or cost. This takes the specific situation of these economies into account, and explains e.g. why some labor market variables are included on the RHS. In a filly functioning market economy, occupation and labor market status must be viewed as endogenous, but this is not the case in many transition economies. Unemployment is high and largely structural, retraining opportunities are limited, and in some countries, the supply of housing is not yet sufficiently flexible to permit easy migration to areas of growing labor demand.

On the other hand, among the asset variables, we have not included ownership of durable goods on the RHS for estimating the three models listed above. This is actually more of a judgment call than it may appear. Until a few years ago, in the countries in our analysis, such goods were rationed. With the possible exceptions of Hungary and Estonia, there is not yet a fully operating market for these goods, accessible to the entire population. Markets for durable goods such as personal computers, VCRs, etc. often exist only in cities, and due to very high relative prices (compared e.g. to Western Europe) accumulation and decumulation of such goods is rare for all but the very rich. For many households, the existing stock is still largely determined by the pre-transition allocation. Nevertheless this situation is rapidly changing.

Generally speaking, asset variables have to be seen as endogenous with respect to household welfare, because in an inter-temporal context, the household's welfare level will determine the extent of education children receive and will determine capital accumulation. For one-period model estimation, based on cross-sectional household data, the case for exogeneity is stronger, but not absolute. In principle, it would be desirable to 15
trace and track the implementation of data localization and associated measures. One is **ECIPE’s Digital Trade Restrictiveness Index (DTRI)** (see Ferracane et al, 2018) and the other one is **OECD’s Digital Services Restrictiveness Index (DGSTRI)** (see Ferencz, 2019). They are therefore both relevant to measure the extent to which regulations in the area of data flows form a potential restriction for trade in services.

In particular, ECIPE’s DTRI covers specifically for various regulatory measures related to data. The regulation measures are split into two parts. The first part covers measures that impact the free mobility of data across countries; the second part deals with the domestic regulatory policies related to the domestic use of data inside a jurisdiction as provided in Ferracane et al. (2018), which also provides explanation of the full list of measures that fall inside the scope of the two parts, including how each measure has been weighted against each other. In addition, the DTRI includes several measures containing categories of restrictions related to cloud computing and digital standards as discussed in the conceptual part above. Ferracane et al. (2018) also provides a list of standards-related policy measures to internet technologies which therefore could have an impact on trade in services performed over the internet. However, it remains unclear to what extent these regulatory measures classify as a TFiS measure.

The OECD’s DGSTRI, which is specifically developed for measuring barriers in digital services trade as opposed to measures affecting all areas of digital trade (including internet technologies) as done by ECIPE’s DTRI, also covers measures on the cross-border mobility as well as standards on payment securities. Measures related to the cross-border mobility of data in the OECD’s DGSTRI are, however, recorded under a broader category of measures that also include regulations related to telecommunications and digital infrastructure, as well as restrictions on the use of communication services. The policies explicitly dealing with data flows are therefore harder to discern. Moreover
Pension Administration**

||Indonesia<br>Pt. Astek|Indonesia<br>Pt. Taspen|Malaysia<br>EPF|Singapore<br>CPF|
|---|---|---|---|---|
|Admin. Expenses as % of:|||||
|_Contributions_|11.7|7.0|2.0|0.5|
|_Pension Assets_|3.2|0.6|0.2|0.1|
|Real Investment Returns|0.0|1.3|4.8|2.9|
|(% per annum)|||||

Sources: World Bank - _Averting Old Age Crisis;_ Factbook on Jamsostek 1994; and data provided by Pt. Taspen.

**_Resource Mobilization_** With low compliance, high lapse rates and poor investment results, resource mobilization under the program has been limited. While similar programs in Chile, Singapore and Malaysia have accumulated assets of at least one-third of GDP, Jamsostek is not expected to mobilize more resources than 2.5 percent of GDP in the next 50 years even if its coverage rises to 80 percent (see figure below).
<br>|661<br>|661<br>|661<br>|
|P-value for test that financial sophistication variables are|||0.87|0.88|0.88|0.90|0.85|

Significance levels: * 10%, ** 5%, *** 1%. Robust standard errors in parentheses.

Notes: Unit of observation is individual included in revisit sample. Spousal characteristics controls are: fraction present biased for all choices, indicators for age category, indicators for education category, word recall, ravens score, financial literacy score, and fraction of choices adhering to law of demand.
Fazzari, Steven M., James Morley & Irina Panovska. **(** 2015). “State-Dependent Effects of Fiscal Policy.” _Studies in Nonlinear Dynamics and Econometrics_ 19(3): 285–315.

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however, considerable variation in these demographic variables despite being drawn from the same population of tobacco farmers in central Malawi.

The summary statistics reveal important features of participants’ wealth and income. When compared to typical households from low-income countries, the households in the sample are poor. In the central Malawi region we study, however, tobacco farmers have similar poverty and income levels to those of non-tobacco-producing households.<sup>22</sup> At the time of the baseline survey, the median participant’s household has zero balances in a formal bank, and the 90th percentile of the bank balance distribution is just 700 Malawi Kwacha (henceforth MK), or approximately US$4.67.<sup>23</sup> The survey also inventoried the household’s non-cash assets including livestock and agricultural and household durables; and participants were asked to estimate how much they could earn by selling those assets. When we include the self-reported value of these assets, we find that the median household held just 4,446 MK of wealth and the 90th percentile held 25,800 MK.

Since the baseline was conducted during the rainy season, several months would elapse before the cash crop or primary staple (maize) would be ready to be harvested in mid-April or early May. The summary statistics bear this out. The median household expects virtually no income between the interview date and April 2010.

# **2.3 Stage One: Choices Under Commitment**

Figure 3 presents the timeline of the experiment. Upon initiating the interview, the household head and his spouse were physically separated. Then, after a few basic questions regarding demographics, each made five independent choices regarding the allocation of 2000MK between tomorrow (“sooner”) and 30 days from tomorrow (“later”).

More precisely, each participant was presented with a small bowl containing 20 beans (tokens) and two empty dishes, dish _A_ and dish _B_ . One token allocated to dish _A_ corresponded to 100MK tomorrow. One token