rafmacalaba/data-use-mentions-extended huggingface dataset:
and 2017 may not be captured by these imputation techniques. In general, this method is more appropriate to estimate poverty for small geographic areas that have no representative samples (e.g. the seminal work by Elbers et al. (2003)), or over short time periods (e.g. Douidich, et al. (2016)).

On the other hand, the pass-through approach assumes that national accounts HFCE growth is accurate and that growth is distribution neutral. Both these assumptions have been the subject of recent debate in India. A. Subramanian (2019) has argued that India’s GDP growth from official sources is overstated, although Goyal and Kumar (2019) have disputed his findings. Regarding changes in inequality, Chanda and Cook (2019) and Chodrow-Reich et al. (2020) find a negative short-term impact of the demonetization introduced in November 2016 among the poorest groups, which dissipates after several months. Lahiri (2020), meanwhile, reports a decline in unemployment shortly after demonetization, which may hide an important decline in labor force participation (also see Vyas (2018)).

The poverty rates estimated for 2017 using the pass-through method would be higher if we allow for increasing inequality, for which there is some supportive evidence in the literature cited above as well as the CMIE data between 2016 and 2017. Assuming a 1 percent annual increase in the Gini index between 2015 and 2017 would lead to a poverty rate of 11.3 percent in 2017, a number still within the confidence interval of the survey-to-survey imputation. If the Gini index were to rise 2 percent per year, the poverty rate would climb to 12.4 percent (compared to 10.4 percent with distribution neutrality) in 2017. If the underlying national accounts growth (in terms of either GDP or HFCE) is reduced by 1.1 or 2.5 percentage points for the period 2015-2017, while assuming distribution-neutrality, we estimate a national poverty rate of 11.0 and 11.8 percent
GEO SRB<br>7.5 8 8.5 9 9.5 10<br>0.1 COL VNM<br>GHA<br>-0.1<br>GHA<br>0 BOL ARM<br>7.5 8 8.5 9 UKR 9.5 10 LKA<br>KEN PHL -0.2<br>-0.1 PHL<br>-0.2 VNM GEO KOS MKD SRB -0.3<br>LAO<br>-0.3 GDP per capita PPP (log) -0.4 GDP per capita PPP (log)<br>F2F index<br>Physical/Manual index<br>F2F index<br>Physical/Manual index<br><!-- End of picture text --> Note: The vertical axis measures the corresponding task index, in standard deviations from the mean for all PIAAC/STEP countries. GDP per capita PPP comes from the World Development Indicators (WDI) and corresponds to the same year of the respective PIAAC and STEP surveys.

The fact that the intensity of jobs on physical/manual tasks tends to decline with economic development, and that the intensity on F2F tasks shows the opposite pattern suggests that two opposing forces are at play when shaping the relationship between WFH measures and GDP per capita. However, F2F occupations also tend to be more intensive in ICT use. As seen in Figure 2, countries such as Singapore or the United States that have jobs more intensive in F2F tasks also use more ICT at work than countries such as Lithuania or Kazakhstan. That is, several of the tasks embedded in such occupations are more prone to be performed remotely. In other words, ICT use at work would tend to weaken the effect of F2F intensity on WFH measures. This correlation between F2F tasks and ICT use at work can also be observed within countries (see Figure A 1 in Appendix 2).

8
the confirmation ratios would not have been roughly equal across inputs. While this evidence is far from airtight, as it is possible the differences in precision could have affected the pattern of statistical significance, the most straightforward way to read this evidence is that studies that use non-experimental data systematically evaluate the marginal product of inputs at points that are not education output maximizing. The results suggest that at the input spending allocations typically observed in non-experimental data in which these allocations are the result of choice, the marginal product of inputs is lower for those inputs more highly valued by teachers and hence they are more likely to be statistically insignificant.

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seen some increase in the proportion of total income earned from wages and salaries. This suggests the increasing importance of large and commercial farms in these regions of Sudan. As seen earlier (Figure 6b), due to the lower rates of poverty for households earning incomes from wages and salaries, their increasing share in the population has contributed to a reduction in rural poverty.

Figure 12: Proportion of Income Earned from Agricultural, Nonagricultural Wages in Rural Sudan, 2009 and 2014/15

# (a) Proportion of Income by Source, 2014/15

<!-- Start of picture text --> 100%<br>Agriculture Non-Agriculture Wages and Salaries<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> _Note_ : Agricultural income comes from crops, horticulture products, livestock, livestock products, poultry and poultry products, fish and fish products, forest products, and other agricultural activities. Nonagricultural income comes from wages and salaries, industrial activities, commerce, rented estates, other rents, transport and communication, transfers from abroad, domestic transfers, supports, and other sources.

- (b) Change in Proportional Income by Source, 2009 to 2014/15

<!-- Start of picture text --> 50%<br>Agricultre Non-Agriculture Wages and Salaries<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>% Δ<br><!-- End of picture text --> _Source:_ Authors’ calculations using NBHS 2009 and NHBPS 2014/15.

The contribution of nonagricultural income to total household income has not increased, either for the poor or the non-poor (Figure 13). In fact, on average, households were slightly less reliant
assumption is valid.

# **7 Concluding remarks**

Many developing countries have recently expanded (or are considering to expand) preprimary coverage as an instrument to stimulate human capital accumulation. Evidence from the United States, Uruguay and Argentina suggests that these investments yield large returns (Cascio, 2009; Berlinski et al., 2008; Berlinski et al., 2009). But this evidence cannot be easily extrapolated to poorer countries, where the potential for policy interventions in this domain is much greater.

We have contributed to filling this gap in the literature by exploiting a large-scale expansion in pre-primary coverage in rural Guatemala from 1998 to 2005. Combining administrative and population census data in a difference-in-differences framework, we have estimated the medium-run impacts of opening a pre-primary on enrollment and adequate progression in primary school by age 12. We find positive effects on the proportion of stu16
economic development: Evidence from China." _Journal of Development Economics 78_ (1): 241-70.

Deininger, K., S. Jin, B. Adenew, S. Gebre-Selassie and B. Nega. 2005. "Tenure Security and Land-Related Investment: Evidence from Ethiopia." _European Economic Review forthcoming_ .

Deininger, K. and P. Mpuga. 2005. "Does Greater Accountability Improve the Quality of Public Service Delivery? Evidence from Uganda." _World Development 33_ (1): 171-91.

Deininger, K. and J. Okidi. 2003. "Growth and Poverty Reduction in Uganda, 1999-2000: Panel Data Evidence." _Development Policy Review 21_ (4): 481-509.

Fitzpatrick, D. 2005. "'Best practice' options for the recognition of customary tenure." _Development and Change 36_ (3): 449-75.

Goetz, A. M. 2002. "No shortcuts to power: constraints on women's political effectiveness in Uganda." _Journal of Modern African Studies 40_ (4): 549-75.

Government of Uganda. 1999. "Report of the Land Act Implementation Study." . Kampala: Ministry of Land, Water, and Environment.

Haque, T. 2001. "Impact of tenancy reforms on productivity improvement and socio-economic status of poor tenants." Policy Paper 13. New Delhi: National Centre for Agricultural Economics and Policy Research.

Hunt, D. 2004. "Unintended Consequences of Land Rights Reform: The Case of the 1998 Uganda Land Act." _Development Policy Review 22_ (2): 173-91.

30
Data on wages and salaries of Central Govemment as percent of GDP is taken from IMF Report SM/94/177 of July 8, 1994 and relates to 1993. GDP per capita, average Government wages, and wages and salaries as multiple of per capita GDP is based upon calculations emanating from this data. Data on wages in manufacturing are from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

|**Bahamas**|Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995<br>and are for 1994. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to 1994<br>and source l:_Labor force sample_surveys and_General household sample_surveys.<br>Central Government, Non Central Government, Education and Health employment are taken from IMF Report No.<br>96/244 and relates to 1995. Police Force is estimated at 1200.<br>GDP at market prices and wages bill estimates are taken fro m IMF Report No. SM/96/244 of September 27, 1996<br>and relates to 1994. GDP per capita, Government average wages and wages and salaries as multiple of per capita<br>GDP are based on calculations emanating from this data.|
|---|---|
|**Barbados**|Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995<br>and are for 1994. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995 and refer to<br>1994 and source l:_Labor force sample_surveys and_General household sample surveys._<br>Data on Central Govemment Employment, Education and Health employment are taken from Republic of Barbados<br>Ministry of Finance and Public Service Commission data and relate to 1995. Local Government in Barbados is non-<br>existent.<br>Employment in State-owned
# 2.9.4 Student and teacher sampling

A number of outcomes of interest, including test scores, are drawn from a sub-sample of students and teachers that are interviewed and tested as part of MLSS. For teachers, the MLSS samples 10 teachers per school at baseline, all of which are re-surveyed at endline if eligible, with additional teachers added to replace those which become ineligible for interview and testing (for example, having left teaching). See section A.0.2 for details.

For students, the study uses a longitudinal sample. At baseline, a gender-balanced random sample of 25 Grade 4 students was sampled in each school. At endline, a subset of 15 students were randomly selected for re-survey at endline.<sup>24</sup> Students who had transferred to new schools were tracked to the new schools, and students who had dropped out were tracked to their homes where possible. The longitudinal sample enables us to measure the impact of the intervention on individual students’ learning trajectories, measured through endline test scores. The median student from the longitudinal sample was in Grade 7 at endline.<sup>25</sup>

# 2.9.5 Power Calculations

We follow convention in the social sciences for power calculations, using a significance level (probability of Type I error) of 0.05 and power (probability of avoiding a Type II error) of 0.8.

We are interested in calculating the endline sample size necessary to detect an effect of _∼_ 0.2 S.D. in student learning, our main outcome of interest, in each of the two specifications.

Our randomization takes place at the school level. We use the mean score for the baseline control group of 500, S.D. of 115, cluster size of 15 students per school. The intra-cluster correlation is 0.28. The required sample size to achieve this effect is 206 (103 in the treatment group and 103 in the control group).
# **References**

<mark>ACLED (2022). “Armed Conflict Location & Event Data Project (ACLED) Codebook”.</mark> <mark>Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement.</mark> _<mark>Journal of public economics</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(7-8), 476-487.</mark> <mark>Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., & Seth, S. (2015).</mark> _<mark>Multidimensional poverty measurement and analysis</mark>_ <mark>. Oxford University Press, USA.</mark> Atkinson, A.B. (2003). Developing Comparable indicators for monitoring social inclusion in the European Union. In: Hauser, R., Becker, I. (eds.) _Reporting on income distribution and poverty_ . Springer, Berlin, Heidelberg.

Bahar, D., A.M. Ibañez and S. Rozo. (2021). Give me your tired and your poor: Impact of a largescale amnesty program for undocumented refugees. _Journal of Development Economics_ , 151: 102652.

Ballet, J., D. Bazin, F. Mahieu (2020). A policy framework for social sustainability: Social cohesion, equity and safety. _Sustainable Development_ 28(5): 1388–1394.

<mark>Ballon, P. (2023). Statistical issues in multidimensional poverty measurement: redundancy analysis. In: Silber, J. (ed.)</mark> _<mark>Research Handbook on Measuring Poverty and Deprivation</mark>_ <mark>(pp. 463-474). Edward Elgar Publishing.</mark> Barron, P
.029)|(0.188)|(0.024)|(0.016)|(0.012)|
|County, Wave, Strata FE|✓|✓|✓|✓|✓|✓|✓|
|<br>Observations<br>|31,187|31,481|31,703|6,095|31,703|28,930|28,389|
|_R_<sup>2</sup>|0.058|0.252|0.501|0.161|0.311|0.140|0.050|

This table shows OLS regression estimates using the first five waves from the Rapid Response Phone Survey data in Kenya. All regressions include the set of controls described in Table 1. Robust standard errors clustered within counties are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

When we interact the household head gender dummy with the two hypothetical channels, and include the channel variables into the regressions, we find further 11
<!-- Start of picture text --> Drought risk<br>Flood risk<br>Seasonal rain oe<br>Rain stress days<br>Std. prec index<br>Used CSA in 2019 —————_—o———_<br>Accessed loan<br>Crop diversity<br>Asset index e<br>Female, hh head<br>Age, hh head @<br>Education, hh head e<br>Credit residuals<br>CSA residuals Or<br>Crop div. residuals<br>-4 -.2 0 2 A<br>Marginal effect (percentage points)<br>Figure shows marginal effect and 95% confidence interval<br><!-- End of picture text -->
there were no comparative data on the magnitude of the brain drain. The ...rst serious e¤ort to put together harmonized international data on migration rates by education level is due to William Carrington and Enrica Detragiache from the International Monetary Fund, who used US 1990 Census data and other OECD statistics on international migration to construct estimates of emigration rates at three education levels (primary, secondary and tertiary schooling) for about 60 developing countries.<sup>2</sup> The Carrington-Detragiache (henceforth CD) estimates, however, su¤er from four main shortcomings. First, CD assumed for each country that the skill composition of its emigration to non-US OECD countries is identical to that of its emigration to the US; for example, Nigerian immigrants in the UK are assumed to be distributed across educational categories in the same way as Nigerian immigrants in the US. Consequently, the CD estimates are not reliable for countries for which the US is not the main destination. Second, at the time CD conducted their study, the OECD immigration data (regarding the EU, Japan, Switzerland, New Zealand...) did not allow for a full decomposition of the immigrants’ origin-mix; more precisely, the OECD published statistics indicating the country of origin only for the top 5 or 10 sending countries. For small countries not captured in these statistics, the ...gures reported in the CD data set are therefore biased: the

> 2See Carrington and Detragiache (1998, 1999). Relying on the same assumptions, Adams (2003) provides estimates for 2000.

5
# **1. Introduction**

The spread of COVID-19 and the implementation of “social distancing” policies around the world have raised the question of how many jobs can be done at home. Most of the existing efforts to estimate these figures rely on US-based measures of the type of tasks required by different occupations (Dingel & Neiman, 2020a; Avdiu & Nayyar, 2020; Mongey, Pilossoph, & Weinberg, 2020; Leibovici, Santacreu, & Famiglietti, 2020).<sup>1</sup> However, the task content of jobs exhibits substantial variation across countries (Lo Bello, SanchezPuerta, & Winkler, 2019; Hardy, Lewandowski, Park, & Yang, 2018). Differences in the organization of production or in the level of technology adoption across countries imply that the same occupation may be more intensive in face-to-face interactions or in physical tasks in poorer economies. As a result, using USbased measures to estimate the amenability to _working from home_ (WFH) in developing countries may lead to biased conclusions.

To overcome this challenge, this paper uses skill and household surveys from 53 countries at different levels of economic development with rich information on the type of tasks carried out by people at work. We estimate indexes of the task content of jobs to rank them by their vulnerability to social distancing measures according to their amenability to a remote setup. In addition, given that the task data vary at the individuallevel—and not by occupation, as in the Occupational Information Network (O*NET) classification—we show how the likelihood of being able to work from home correlates with other characteristics of the individual and his or her job.

We build on the literature by estimating jobs’ amenability to WFH, as opposed to estimating the fraction of jobs that can be done at home. Estimating the latter is challenging since choosing the tasks that determine whether a person can work from home is largely arbitrary, specifically if one does not have a model linking such tasks to the probability of WFH during
needed to create retrospective asset ownership. We illustrate that these data carry us a considerable distance in the direction of mimicking the “ideal” set-up explored in Lanjouw et al (2011), thereby strengthening the practical appeal of this synthetic panel approach.

Third, our diverse settings and panel data allow us to test the performance of different interval lengths between the two rounds of cross-sectional surveys, from a one-year interval in Peru to an interval of ten-years in Chile. This is a key question for mobility analysis as it is important to know whether the technique can provide accurate predictions of mobility both in the short-term as well as the long-term. In fact, and as mentioned above, while short-term panel data are relatively common in developing country settings, they rarely exceed more than three years in length, making _any_ analysis of long term mobility almost impossible. Evaluating the performance of this approach for long term mobility is therefore crucial.

Finally, Lanjouw et al. (2011) include only variables in levels as regressors in the underlying models of consumption. This paper introduces a variety of interactions between time invariant household characteristics and geographical controls and regional fixed effects (suggested by Lanjouw et al. 2011, but not applied). We find that the introduction of such interactions indeed increases the predictive power of models and thereby narrows the bound estimates yielded by the approach.

In all, our results indicate that the methodology performs well in predicting a range of mobility measures by means of two rounds of cross-sectional data; _true_ mobility lies within the two bounds in most of the cases studied. We find that the particular specification of the underlying model of income/consumption matters for reducing the bounds, with considerable progress possible following the introduction of interaction terms and retrospective asset 4
abandon their anonymity, a digital registry platform emerged as the most viable solution and the SAR, interoperable with registry and cadaster, was launched by August 2022.

The PSG, which provided a cash grant to small farmers (below 120 ha) proportional to the amount of land cultivated in 2022, was one of the first programs using the SAR. Although it provides interesting qualitative insights, high rates of non-response and attrition reduced the representativeness and power of a phone survey. Combining administrative data with use of the universe of eligible SAR registrants thus provides an alternative way to explore PSG targeting and impact. As resources were made available on a first come, first served basis, analysis of impacts on area cultivated is more challenging methodologically. We use the pool of farmers whose digital application was lodged too late to be processed before PSG resources had been exhausted as a control group and use weights from entropy matching to establish comparability in observables between treatment and control. Parallel pre-trends cannot be rejected and insignificant coefficients from a placebo test allay concerns about differences in unobservables driving our results.

2
countries, Cusolito, Lederman, and Pena (2021) find large positive effects of digital technology adoption on firm productivity.<sup>2</sup> Since digital goods are mainly produced in advanced or large emerging economies such as China, the quality and prices at which firms and consumers in developing countries can acquire them in international markets are especially important. However, there is little systematic evidence on the quality and prices of imported digital goods in low-income countries, or on the degree of tariff protection they face. A recent study using item-level prices from the International

> 1Fan, Li, and Yeaple (2015) also find that Chinese firms responded to reduced tariffs on imported inputs by raising export prices and quality, and that this effect is stronger in more differentiated sectors. Bas and Paunov (2021) find broadly similar results with representative data from Ecuador (linking customs data to a more traditional plant panel survey), and in addition that imported-input driven upgrading is stronger in more skill-intensive firms and is in turn associated with increases in skill intensity.

> 2As summarized in Dutz, Almeida, and Packard (2018), recent related research for Latin America concludes that digital technology adoption offers a pathway to higher productivity. Total factor productivity of technologyadopting firms increased in Argentina, Brazil, Chile, Colombia, and Mexico (Brambilla and Tortarolo, 2018; Iacovone and Pereira-Lopez, 2018; Almeida, Fernandes, and Viollaz, 2017; Dutz, Mation, O’Connell, and Willig, 2017).

1
CDR analysis can complement conventional research methods by enhancing the statistical strength of survey data and by extrapolating these data across time and space. The analysis presented above was limited to encrypted and aggregated CDR data from just five administrative departments in southwestern Guatemala, and the results suggest that expanding the sample size would allow for more robust and reliable poverty estimates. Policy makers in Guatemala could obtain more comprehensive datasets by working directly with MNOs.

As analytical methodologies are further developed, CDR applications could extend well beyond poverty mapping. CDRs could enable policy makers to track patterns of crime, food insecurity, epidemic disease and other social and economic shocks in real time. Policy makers in Guatemala and other developing countries now have the ability to access a wealth of cellular data. Establishing strong partnerships with mobile operators will be the first step in harnessing the enormous potential of cellular data for socioeconomic research and policy analysis.

**21**