rafmacalaba/data-use-mentions-extended huggingface dataset:
informal borrowing (from relatives, friends, acquaintances and work).

Table 12 presents these findings. For each of these two aggregate outcomes, we consider three different specifications. In the first specification we use as the dependent variable the total loan amount obtained from the survey data as it is, without any corrections for possible mis-reporting on behalf of the respondent. Next, we add to the regression a control for whether or not we consider it likely that there is an inconsistency between the household respondent’s report on borrowing and household’s actual borrowing behaviour. To obtain an estimate of the household’s tendency to mis-report, we compare official administrative credit bureau data on uptake of MFI loans over the study period (yes or no) to the household respondent’s own report of MFI loan take up over the same period. We create an inconsistency indicator equal to one if we observe any inconsistency between the two measures and zero otherwise. We then add this indicator as a control to the regression models in Table 12 (columns 2-3 and columns 5-6). In addition, in columns 3 and 6, for households that report to have taken a positive loan amount (from other formal or informal sources, respectively) we use household’s specific discrepancy between its respondent’s report on MFI loan amounts and credit bureau data records on the same to construct a loan amount measure corrected for possible mis-reporting.<sup>33</sup> Overall, we do not find any impacts on borrowing from

> 30For the measures that rely on self-reported survey data, we conduct a set of robustness checks that take possible misreporting into account.

> 31This information is not reported in the credit bureau data. Information on the interest rates charged by other MFIs in the study area, obtained from their webpages after the endline survey, suggests that the interest rates charged by our partner MFI were similar or lower than those charged by other MFIs.

> 32This is a potentially disconcerting finding. To analyse the potential impact of this
either all ones or all zeros to observations where the ratio is undefined. Imputing all ones assumes that, if cases had reported, they would have all been confirmed; imputing all zeros assumes that, if cases had reported, none would have tested positive. Figure E.9(a) plots the average ratio of confirmed to total cases across control and treated sections. Looking at either bound, there is no meaningful difference in these ratios, and the confidence intervals overlap throughout the study period.<sup>46</sup> In Section E.20, we write down a model to clarify what must be assumed for our results to reflect a change in exposure (as opposed to reporting). For confirmed cases to increase while the share of confirmed to total to remains unchanged, one must conjecture that the treatments dramatically increased reporting by asymptomatic individuals while having negligible effects among those showing possible signs of the virus. This strains credulity: one cannot preemptively test for Ebola, so individuals without symptoms have no reason to report; moreover, qualitative accounts suggest the crisis deterred unexposed individuals from visiting clinics, even when they had other healthcare needs (Elston et al. 2016).

# **4.3.2 Surveillance**

Surveillance is challenging in a setting like rural Sierra Leone: if individuals or families want to avoid health workers, they are unlikely to be detected given the difficulties of canvassing sparsely populated regions with limited road networks (Richards et al. 2015; Olu et al. 2016; McNamara et al. 2016). Nonetheless, we look for indications of intensified top-down efforts in treated sections as a possible alternative explanation for the increase in reported cases.

First, contact tracing is central to disease surveillance efforts. In our control sections 59 percent of confirmed cases were subject to contact tracing, compared to just 22 percent in CM and 28 percent in NFA (Table E.16). Second, we use three measures derived from the VHF data (all measured at the section-level
# **5.2 Notes on Interpretation**

In the following section, we report urban trend results for September 2014 – December 2021 and dummy-variable results for two five-year periods: January 2015 – December 2019 and January 2017 – December 2021. Before presenting the results, we believe that some interpretive notes are warranted. First, we offer a caveat about viewing long- and short-period differences across urban areas as indicators of differential performance, because “performance” implies intentionality on the part of public or private actors. However, even highly-significant changes in local concentration anomalies may reflect non-intentional factors such as changed agricultural practices, blight-induced forest degradation, or additional emissions from traffic congestion during periods when mass transit systems are installed. Measurement anomalies may also intrude, particularly for model (7) because it focuses on relatively short-run changes.<sup>10</sup> To summarize, it may be more useful to view tracking results as guides to detailed local assessments rather than as performance indicators.

A note about measurement units is also warranted. In Dasgupta, Lall and Wheeler (2022), we convert concentration anomalies to emissions estimates using the overall ratio of total global CO2 emissions (drawn from standard sources) to the total for all grid squares of concentration anomalies predicted from the parameters of our econometric model. This enables us to perform a distribution of predicted CO2 emissions across all terrestrial cells of the 25 km global grid. However, we have little confidence in our current ability to infer changes in emissions volumes from directly-observed changes in local anomalies that are not linked to identifiable ground sources. For this reason, our trend estimation exercise operates solely with concentration anomalies. This has no practical consequence, since the results provide readily-comparable change estimates.

# **5.3 Tracking Data**

We estimate models (6) and (7) for 1,799 functional urban areas (FUAs) whose data provide at least 30 degrees of freedom for estimation.<sup>11</sup> Table 4 enumerates the outlying
S2 presents the detailed results.

> 28These results also hold within sector-occupations in Brazil, the only administrative data set used in the section that contains information on occupations. Decomposing the variance within-sector-occupations into between- and within-firm components (as in Helpman et al. 2017, who carry out a similar analysis for 1986–95), this paper finds that the decline of wage inequality is largely accounted for by a fall in between-firm wage dispersion. The results are available from the authors upon request.

27
suggests that the households had a low income elasticity of test scores, it is impossible to make any further conclusions without further information on what the household spent the extra cash on. Specifically, given the declining marginal benefit of spending on education in each particular period, the household may still have found it better to save this money for spending on future educational materials, rather than just spend it on goods and services this year. As we do not have information on what the households did with the extra cash available, we cannot explore this further.<sup>39</sup> We summarize this section by noting that while having further disaggregated data on both school and household expenditures would allow for an even more precise understanding of the mechanism for our test score results, the combination of the household spending results and the test score results are most parsimoniously explained by the theoretical framework laid out in this paper. We consider but end up rejecting several alternative explanations for these results and finding the same results in two contexts as varied as Zambia and India makes us confident that our results present evidence of differential household responses to anticipated and unanticipated school grants.

# **6 Conclusion**

Data on test-scores and household expenditures in the context of school grant programs in Zambia and Andhra Pradesh in India suggest that grants anticipated by households crowd-out private educational spending. Consequently, school grants that are fully anticipated have no impact on test-scores. Unanticipated grants elicit no household responses and do have positive impacts on learning.

These results have implications for common estimation techniques in the educational literature. The dominant technique for estimating the effect of school inputs on test scores is based on the production function approach, where achievement (or changes in achievement) is regressed on school inputs. Following Todd and Wolpin (2003), these estimates represent the policy effect of school inputs that combines both the effect of inputs on test scores through the production function, as well as household responses to such inputs. Our use of unanticipated

> 39 What we _do_ know from
the mean with the adjusted distributions. The population-weighted average annualized shared prosperity premium for the period falls by 0.6 percentage points, from a positive 0.5 percent growth to -0.1 percent, inverting the positive global picture of trends in inequality measured by the official spells. This further illustrates that conventional measures of development can be misleading if one assumes the HHS-NAS gap to originate from a lack of capture of consumption and income of the richest households in surveys.

# **5. Conclusion**

This paper has compiled a large new data set for assessing the correspondence between per capita monetary living standards measured in national accounts and household surveys. The data show that the gaps in measurement across the two data sources are larger than in previous assessments. Our assessment concludes that the gap does not seem to be due to survey income 27
0.788|0.627|0.981|0.669|0.597|0.989|0.949|0.912|0.783|0.916|

Note: This table reports district fixed-effect RDD-DD regressions of infrastructures of communes using commune-level observations. Robust standard errors in parentheses. Standard errors are clustered by district level. *** p<0.01, ** p<0.05, * p<0.1.

46
# **B. ECONOMETRIC METHODOLOGY**

The model discussed above is estimated using the following general framework in which the real effective exchange rate in the CEMAC and UEMOA zones is viewed as a function of a vector of fundamentals- terms of trade, openness of the economy, net aid inflows, and government consumption. The dependent variable of the analysis is the log of the real effective exchange rate, essentially a trade-weighted index derived from 1970 to 2005. All variables were put in logarithmic form to ensure that parameters represented elasticities, and annual time series data were used to estimate the following model:

# (3) RER = f (TOT, OPEN, AID, DUMMY)

The two principal techniques for the time series estimation of the long-run relationship and cointegration of two variables are the Engel-Granger (1987) and the Johansen methodology (1988), the key diagnostic tools to test for systemic patterns in time series. The essence of cointegration is that even if certain time series themselves are nonstationary, a linear combination of them will be stationary, and that there is a long-run co-movement between the variables. The Engel-Granger technique is a two-stage method that examines cointegration and is based on the assumption that a single cointegration relationship exists, which can be estimated through ordinary least squares.

First, standard unit root tests were performed on all the macroeconomic variables prior to the estimation to ensure that the two variables are integrated of the same order because traditional estimation and inference procedures based on traditional t and F tests do not apply in the presence of nonstationary variables. The optimal lag lengths were determined using the Akaike Criterion. The Augmented Dickey-Fuller test was used to examine whether one can reject the null of a unit root at traditional significance levels. Econometric tests were conducted in order to assess whether the variables were first-difference stationary.

Secondly, the following the Engle-Granger methodology, the long-run equilibrium equation was tested using ordinary least squares (OLS) relating the levels of the real effective exchange rate to the economic fundamentals that affect it (Seddighi, Lawler
TABLE 111.5. MAKING COMPATIBLE THE NET FEDERAL DEBT STATISTICS AND THE NOMINAL DEBT STATISTICS IN R$: 1995-1998

|In R$ (millions)|Dec/94|Dec/98|Variation|Percentage<br>Variation|
|---|---|---|---|---|
|Net Federal Debt (+ Central Bank)|65,836|231,258|165,422|251.3%|
|+State's and Municipalities' Net Debt|51,091|130,905|79,814|156.2%|
|+ State Owned Enterprises' Net Debt|36,236|26,504|-9,732|-26.9%|
|=Total Net Debt|153,163|388,667|235,504|153.8%|
|-Balance Sheet Adjustment|0|39,516|39,516||
|+ Privatization Adjustment|0|30,656|30,656||
|=Net<br>Fiscal Debt without<br>Privatization|153,163|379,808|226,645|148.0%|

TABLE I11.6. MAKING COMPATIBLE THE NET FEDERAL DEBT STATISTICS AND THE NOMINAL DEBT STATISTICS IN percent OF GDP: 1995-1998

|In percent of GDP|Dec/94|Dec/98|Variation|
|---|---|---|---|
|Net Federal Debt (+ Central Bank)|12.25%|25.34%|13.10%|
|+State's and Municipalities' Net Debt|9.50%|14.35%|4.84%|
|+ State Owned Enterprises' Net Debt|6.74%|2.90%|-3.84%|
|=Total Net Debt|28.49%|42.60%
# **Combining Survey and Geospatial Data Can Significantly Improve GenderDisaggregated Estimates of Labor Market Outcomes** Joshua D. Merfeld<sup>*</sup> , David Newhouse<sup>†</sup> , Michael Weber<sup>‡</sup> , and Partha Lahiri<sup>§</sup>

JEL codes: J21, C13 Keywords: Small Area Estimation, Data integration, Geospatial Data, Labor Force Participation, Unemployment, Mexico

* KDI School of Public Policy and Management and IZA

- The World Bank and IZA

- The World Bank and IZA

> § Department of Mathematics and Joint Program in Survey Methodology, University of Maryland
Turning to the firms, female firms are smaller and more likely to operate with no employees (39% of sampled female-led firms have no employees versus 23% male-led firms).<sup>25</sup> On average, 64% of female-led enterprises in the sample have at least one employee, relative to 80% of male-led enterprises. In terms of firm size defined by the number of employees: male enterprises in the sample comprise of: 7.5% medium/large, 33.5% small, 39% micro, and 20% zero employees; and female enterprises comprise of 5% medium/large, 20% small, 39% micro, and 36% zero employees. The survey asked retrospective questions on firm revenues to capture firm size before the pandemic and the pre-pandemic average annual revenues (2019) for a female-led business is USD74,102 which is approximately 35% lower than a male-led business at USD114,392. In terms of the role in the business, 47% of respondents reported to both own and manage their business, 38% own the business; and 15% manage the business. In terms of industry, approximately 45% of male-led firms operate in the services and retail/wholesale sectors compared to 55% of female-led firms; with a greater proportion of men operating in the professional services and ICT sectors compared to women.

The sample of respondents of the Future of Business survey should be considered entrepreneurs with at least some digital access and the survey sample was designed to be representative of the Facebook Business Page administrator population and not of any country’s business population. Nevertheless, the level of geographic coverage and sample size on the variables collected by the survey allow us a unique chance to examine the COVID-19 shock for a group of micro, small and medium enterprises on a global scale. Importantly, this survey was done at a time when traditional data collection methods were not possible. Since internet bandwidth and Facebook coverage have increased dramatically in recent years, with
category of trade costs – that relating to infrastructure and border costs, captures the cost of delivering services under Modes 1, 2 and 4 from suppliers to customers. Such costs remain significant, accounting for a fifth (20 percent) of trade costs in services.

Figure 9 offers a sectoral breakdown of trade costs, once more solely for services trade under Modes 1, 2 and 4. As can be seen, trade cost components show considerable cross-sectoral variance. For instance, the effects of information and transaction costs are more pronounced in sectors such as transport and logistics, as well as for post and telecommunication services. Governance quality and trade policy barriers play a major role in the trade costs of telecommunications, financial intermediation and professional services. Meanwhile, infrastructure, transport and border costs matter more for construction, real estate activities, education, health and social work.

The WTO’s analysis of the different cost components affecting trade in services under Modes 1, 2 and 4 inevitably raises the question of how best to measure trade costs for Mode 3 transactions. This question is all the more important since establishing a commercial presence in a host country often entails not only the adoption of a different (i.e. adapted) business model for services suppliers, but also compliance with differing economic, political, social and even environmental requirements in host country markets, all of which are likely to affect the cost of trade or, in this case, the cost of serving clients through an established presence abroad.

Contrary to the other three modes of supplying services, where WTO estimates show cost components associated with the quality of governance to be of lesser significance, recent research undertaken by the World Bank Group shows that the opposite holds true for Mode 3 trade (Echandi, 2019). Indeed, as noted earlier, firm-level surveys reveal that as much as one-quarter of all FDI projects in services may be totally 20
- Sanchez, G. (2020). Migrant smuggling in the libyan context: re-examining the evidence. In P. Fargues and M. Rango (Eds.), _Migration in west and north Africa and across the Mediterranean: Trends, risks, development and governance_ , pp. 230–238. Berlin: IOM GMDAC.

- Shrestha, M. (2019). Death scares: How potential work-migrants infer mortality rates from migrant deaths. _Journal of Development Economics 141_ , 102368.

- Tjaden, J. and F. A. Dunsch (2021). The effect of peer-to-peer risk information on potential migrants–evidence from a randomized controlled trial in senegal. _World Development 145_ , 105488.

- UN-DESA (2020). International Migrant Stock Database. Department of Economic and Social Affairs of the United Nations Secretariat, New York (USA).

- UNHCR (2017a). _Global Trends: Forced Displacement in 2016._ United Nations Human Rights Council. Geneva (CH).

- UNHCR (2017b). _Mixed Migration Trends in Libya: Changing Dynamics and Protection Challenges._ United Nations Human Rights Council. Geneva (CH).

- UNHCR (2019a). _From hand to hand: The migratory experience of refugees and migrants from East Africa across Libya_ . United Nations Human Rights Council, Geneva, CH.

- UNHCR (2019b). _Mixed Migration Routes and Dynamics in Libya in 2018_ . United Nations Human Rights Council, Geneva, CH.

- UNSMIL (2018). _Desperate and Dangerous: Report on the human rights situation of migrants and refugees in Libya_ . Office of the United Nations High Commissioner for Human Rights (OHCHR) and United Nations Support Mission in Libya (UNSMIL).

- Wilson, R. (2021). Moving to jobs: The role of information in migration decisions. _Journal of Labor Economics 39_ (4), 1083–1128.

- Wittenberg, L. (2017). _Managing Mixed Migration: The Central Mediterranean Route to Europe_ . New York: International Peace
# **What types of firms perform better than others?**

In this section we account for the variation in productivity (TFP) across firms that can be attributed to various characteristics of firms. The key characteristics of interest are ownership and export orientation; it is particularly important in the Serbian context to establish that, based on international experience, private ownership enhances productivity. The previous section also established that the productivity of firms is highly correlated with the country in which they operate. The analysis here augments the estimation equation used in column 2 in Table 1 with measures of ownership, export orientation, and also location (whether or not the firm is operating in a capital city). In the regression results, the estimated impacts on TFP of these firm characteristics are impacts that raise or lower the TFP of a firm compared to other firms in the same country.

**_International evidence._** The estimation used a sample of firms from 51 PICS-BEEPS surveys of firms in 44 countries. The estimations were done separately for manufacturing and services firms (Table 2). The omitted or benchmark category is state-owned, domestic, non-exporters and not in a capital city.<sup>12</sup> The results show that privately-owned firms, whether privatized or new private, have levels of TFP that are 30-38 percent higher than state-owned firms in the same country. Foreign ownership adds another 23-43 percent to TFP levels. Export orientation adds 22 percent to TFP (for manufacturing firms only).<sup>13</sup>

**Table 2: TFP regressions using firm-level data**

||**Manufacturing**|**Services**|
|---|---|---|
|Fixed capital|0.374**|0.268**|
|Labor|0.686**|0.698**|
|Privatized|0.382**|0.295**|
|New private|0
# IMI. Framework, Data, and Variables

In this section, we discuss a framework for analyzing the determinants of school enrollment and completion levels by reviewing research pertinent to our study, and drawing specific hypotheses from models of schooling choice. We then present the empirical model and relationships to be estimated, and briefly discuss the sample drawn for this analysis from the Peru Living Standards Survey (PLSS).

# The Schooling Decision

Three classes of research literature are relevant to our study. One is the literature on returns to education or why people invest in schooling; another is the literature that deals with who makes the schooling decision(s); and the third pertains to the process of education itself. Each of these literatures is vast and rich. We do not attempt to review them exhaustively, but to provide a brief summary of their principal hypotheses and findings.

Returns to education models. There are two competing hypotheses in the economic literature about returns to education. The human capital model asserts that the principal benefit from education is that it raises a person's productivity, whether in the workplace or at home, at work or at play. In the workplace, this increase in productivity translates into higher earnings (Becker, 1964; Mincer, 1974, among others); at home, it means more efficient home production, such as child care (Gronau, 1977). Also related to this model are the hypotheses that more highly educated people learn (that is, 12