rafmacalaba/data-use-mentions-extended huggingface dataset:
*|**1.37%**|**1.30**|**2.19**|

Source: our elaborations of the EBCNV 2015.

During the first planning phases of the new EBCNV 2021, the INS initially took into account the same three aspects for determining the sample size: i) the information of the population at that time; ii) the _explicit stratification_ of the PSU in the design, and iii) the expected maximum error of 10% at the governorate level. These would have resulted in a sample size of about 29,000 households and a total cost of about 6.9 million dinars.

Given the comparative benchmark of 6.9 million dinars and the benchmark of expected maximum error of 10% at the governorate level, the aim of the new sampling design has been to reduce both costs and maximum errors. Table 5 reports results from three scenarios, with different survey designs in terms of design effect and sample sizes needed to reach specified maximum percentage errors (left column). The three scenarios are as follows:

- a) <u>Explicit stratification only: PSUs are stratified in the 24 governorates, and</u> then selected with simple random sampling; this is an unrealistic scenario nowadays, even in household surveys in Africa. However, the expected standard errors are usually calculated hypothesizing this scenario so that the implemented sample sizes are overestimated for the purposes of the survey with very high costs.

- b) <u>Serpentine: PSUs are first stratified in the 24 governorates, and then</u> further stratified (implicitly) with systematic sampling by ordering the lists

17
online appendix

**Domestic mask prices** To account for the composition of domestic mask sales, we construct prices as mean log prices at the month-by-firm-product level. We let _pft_ be the mean log price of firm-product _f_ sold domestically in month _t_ . We then estimate

where _τt_ captures the change in log mask prices relative to January 2020, and _αf_ is a firm-product fixed effect that controls for any changes in the composition of masks sold domestically. We report standard errors clustered at the sub-District of the firm. As discussed in Section 2.2, starting April 2020 sales of domestically manufactured multi-layer masks grew relative to sales of imported medical masks.

**Border mask prices** For border prices, we similarly construct mean log prices per unit at the month-by-firm-border post level. Since unit mask prices are not naturally identified in the customs data, we prices per unit of weight, net of freight weight.<sup>A7</sup> Further, as we are unable to consistently identify products in the customs data, we use firm-border post fixed effects to account for changes in the composition of mask imports. We let _p_<sup>imp</sup> _ft_ be the mean log price of imported masks in customs data for firm-border post _f_ in month _t_ .

where _τt_<sup>imp</sup> captures the change in log border prices of masks relative to January 2020, and _αf_ is a firm-border post fixed effect that controls for any changes in the composition of imported masks, assuming that variation in quality of imported masks and other unobservable determinants of price does not systematically vary over time

> A7See Appendix A.2 for additional details on this construction.

A12
show that ICT investment contributed to economic convergence between Central and Eastern European countries with the EU-15 and the United States during the 1990s. Intangible capital is found to be positively correlated with growth in China (Hulten and Hao 2012), export growth and TFP growth in the manufacturing sector in Brazil (Dutz et al. 2012), and software sector output in India (De 2009; De and Dutta 2007). In a study of 60 economies between 1995 and 2011, Chen (2018) finds that the differences in intangible capital can account for up to 16 percentage points of cross-country income variation.

# **3. Digitalization, employment, and wages**

Most people benefit from economic growth through the income they—or someone in their household—earn from work. This is particularly the case for poor households, whose human capital is often their largest asset. Digitalization, much like any other technology, can affect people’s incomes to the extent that it automates certain tasks, consequently displacing workers from certain jobs, or by placing a premium on new skills that poor or disadvantaged households often do not possess.

## **3.1** **_Does automation reduce the demand for labor?_**

The effects of technological change on the demand for labor have concerned society since the dawn of automation. A task-based framework assessing three channels through which digital 11
Policy Research Working Paper 9353

# **Abstract**

Export responses to real exchange rate (RER) depreciations in Pakistan are lower than those to appreciations. This paper empirically documents this asymmetric response using macro-level data. It then relies on a disaggregated export product–level data set for 2003–17 to test, within a panel fixed-effects framework, three hypotheses explaining the low export response to depreciations, focusing on information costs, supply constraints, and pricing to market. The analysis finds that (i) exports of differentiated products grow more slowly when the RER depreciates than they fall when it appreciates; (ii) exports from sectors with relatively greater supply constraints—in particular related to accessing finance- respond less to depreciations than to appreciations; and (iii) dollar prices for Pakistani exports tend to fall after nominal depreciations of the Pakistani rupee, in violation of the Dominant Currency Paradigm and consistent with pricing-to-market behavior, further accounting for the low response of exports to RER depreciations.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 gvarela@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 authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._
Police (20,000), and the Border Security Guard (8,900). Data on wages in manufacturing (monthly basis) are taken from the Intemational Labor Office's Yearbook of Labor Statistics 1995 and refer to 1993.

|**Spain**||
|---|---|
||Paid employment in non-agricultural activities is drawn from the ILO's Yearbook of Labor Statistics1995and refers|
||to1994.|

Data on Data on Central Government, Non central government, Education and Health employment are taken from Public Management: OECD Country profiles, OECD 1992 and relate to 1990. Public education and health are the responsibility of non-central government. Military employment data include conscripts (126,000), but exclude personnel in paramilitary units, i.e., Guardia Civil (75,000), and **la** Guardia Civil del Mar (550). Average Govemment wages is taken from Trends in Public Sector Pay in OECD Countries, 1995 edition and relates to 1993.

Data on wages in manufacturing (monthly basis) are taken from the International Labor Office's Yearbook of Labor Statistics 1995 and refer to 1994.
use measures and the covariates that are commonly used in empirical research as proxies for individual socioeconomic status (or bargaining power, in the context of intra-household analyses). These knowledge gaps are in part due to the weaknesses in the availability and quality of individual-disaggregated survey data collected in large-scale, multi-topic household surveys. These surveys seldom collect individual-disaggregated information on men’s and women’s asset ownership and time use, together with data on employment and complementary socioeconomic and demographic characteristics. Without such integrated data collection, it is not possible to fill the aforementioned knowledge gaps.

However, even when the data are available, poor data quality can impede development research. For example, in large-scale household surveys, it is common for the data on asset ownership to be either collected at the household-level – even when assets are owned by individuals – or collected at the assetlevel, with individual disaggregation to identify owners and right holders, whom would, however, be identified by only one “most knowledgeable” household member. Distortionary effects of proxy respondents have in turn been documented in the measurement of not only men’s and women’s asset ownership but also employment (Kilic et al. 2021; Kilic et al., 2020), underlining the importance of eliciting self-reported survey data on these topics.<sup>4</sup> Proxy reporting and other measurement challenges on individuals’ time allocation have also marred a clear understanding of the varied activities women and men are involved in throughout the day, and how their time is linked with other socioeconomic factors (Floro, 2021).

> 3 This includes how time in paid and unpaid work is affected by individuals’ share of earnings in the household (see Bittman, 2003, using data from the U.S. and Australia, and Sevilla-Sanz et al., 2010, using data from Spain); unearned income from transfers and property (Schultz, 1990); cash transfers (Hasan, 2010, examining a program targeted towards girls’ education in the Punjab province
and Zeufack, 2020). Tables 3 and 4 provide summary statistics for the SCI indicator for both the panel and cross-section data sets, respectively. Tables 5 and 6 present the correlation between the SCI and other variables for both cross-sectional and panel data,

> 6 The discrepancy between vaccination rates from government administrative records and household surveys has been documented by Sandefur and Glassman (2015) as an indicator of bad government data.

16
**Figure 13. Most Advantaged and Least Advantaged Simulations for Nutrition by Year (Percentages)**

<!-- Start of picture text --> 2003/4 2006/7 2011<br>100<br>90<br>80<br>70<br>60<br>50 44<br>38<br>40 33<br>30 28<br>30<br>20<br>10<br>10 5 7 3 6 6<br>0 0 1<br>0<br>Stunted Underweight Wasted Iodized salt<br>Percentage<br>Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged Least Advantaged Most Advantaged<br><!-- End of picture text --> _Source:_ Authors’ calculations based on DHS 2003/04, MICS 2006/07, and ENPSF 2011.

- 4.3.3 Cognitive, Social, and Emotional Development

Children’s social, emotional, and cognitive development shows some of the greatest and most persistent inequalities (Figure 14). As of 2006/07, the inequality (as measured by the dissimilarity index) was 34 percent of opportunities needing to be redistributed for ECCE, 14 percent for development activities, 30 percent for violent child discipline, and 5 percent for work at age five (including domestic work). By 2012, inequality fell for ECCE. However, inequality for development activities had risen by 2011. No data are available for comparing violent discipline or work (including domestic work) over time.

19
��<br>_NREGAd,t_=1||1.724|
|||[1.063]|
|All Controls|Yes|Yes|
|Observations|1340|1340|

_Source_ : Author’s analysis based on AWI data (for field wages) and program expenditure data for SGRY and NREGA from 2001 to 2010. Program expenditure data is from the Ministry of Rural Development, Government of India. _Note_ : All estimates are of the order 10<sup>_−_2</sup> . Column 1 shows the wage elasticity as reported in column 4 of table 1. Column 2 reports separate elasticities for SGRY and NREGA regimes. All other controls are the same as in table 1 in the main article. Regressions are weighted by district-level population. The standard errors reported in square brackets are clustered at the district-level and robust to heteroskedasticity. SGRY: Sampoorna Grameen Rozgar Yojana. NREGA: National Rural Employment Guarantee Act. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01
79 Figure A12: Distribution of Landholding Sizes <!-- Start of picture text --> −6 −4 −2 0 2 4 −5 0 5<br>Log of landholding size (in ha) Log of landholding size (in ha), residualized<br>(a) Unadjusted (b) Adjusted for Region<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> _Notes_ : The figures show kernel density estimates for (the log of) the size of households’ landholdings (measured in ha). A unit of observation is a household. The data comes from the 2016 edition of the HIES. Panel A12a shows unadjusted estimates. Panel A12b shows estimates for a residualized version of the log landholding size variable which is obtained by extracting the residual from a regression of that variable on a set of district dummy variables, with the regression estimated by Weighted Least Squares (WLS) using the statistical weights provided by the HIES. Densities are estimated using Epanechnikov kernels with optimal bandwidth (0.2021 in Panel A12a and 0.1647 in Panel A12b) and statistical weights.

sample. We then estimate the dispersion parameter _σLn_<sup>2for district</sup><sup>_n_as:</sup> where _|_ S _n| ≡_<sup>�</sup> _h_<sup>_′_</sup> _∈n_<sup>1 is the number of surveyed households in district</sup><sup>_n_that own land.</sup>

Given estimates _σ_ ˆ _Ln_<sup>2ofthedispersionparameters,wecanthenestimatethescaleparameters</sup> _μn_ so as to match the average landholding size in each district. Specifically, the properties of the log-normal distribution imply that the average landholding size in district _n_ is exp
Policy Research Working Paper 9786

# **Abstract**

In this paper, the authors: (i) study wage-experience profiles and obtain measures of returns to potential work experience using data from about 24 million individuals in 1,084 household surveys and census samples across 145 countries; (ii) show that returns to work experience are strongly correlated with economic development—workers in developed countries appear to accumulate twice more human capital at work than workers in developing countries; (iii) use a simple accounting framework to find that the contribution of work experience to human capital accumulation and economic development might be as important as the contribution of education itself; and (iv) employ panel regressions to investigate how changes in the returns over time correlate with several factors such as economic recessions, transitions, and human capital stocks.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 aislam@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 authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
of the satellite imagery to improve poverty predictions. The benchmark used for evaluation is the municipal income poverty estimates, generated by CONEVAL using a combination of the 2014 MCSENIGH household survey and the 2015 Intercensus survey. The Intercensal survey is conducted every 5 years between two censuses, to update socio-demographic information at the national and

> 11 AGEBs (Áreas Geoestadísticas Básicas) are equivalent to sub areas in this context, while municipalities are target areas, or the areas at which we are interested in predicting poverty. Each municipality is made up of AGEBs, with larger municipalities generally having more AGEBs.

12
# **4.4.2 Decomposition results**

Table 8 presents the decomposition results. Panel A reports the change in the OLS coefficient on a dummy variable for India when a linear control for village-level open defecation is included, as in table 5. Panel B reports the change in the unexplained difference when open defecation is added to a weighted Blinder-Oaxaca decomposition. Panel C presents counterfactual differences from non-parametrically weighting the Indian sample to match the distribution of village and household open defecation in the African sample.

Columns 1 and 2 present the basic result: the simple sample mean with no controls, with and without adjustment for sanitation. Village-level open defecation linearly explains 99 percent of the India-Africa gap.<sup>27</sup> In the Blinder-Oaxaca and non-parametric decompositions, sanitation explains more than 100 percent of the gap; this “overshooting” is plausible because, in addition to having worse sanitation, Indian households are richer, on average, than African households.

The next four pairs of columns similarly find that open defecation explains much of the India-Africa gap after controls are added. Specifically, columns 3 and 4 control for demographic variables before decomposing the remaining gap: sex-specific birth order indicators and an indicator for single or multiple birth.<sup>28</sup> Columns 5 and 6 first control for a vector of socioeconomic controls, the same controls used earlier in equation 3. Columns 7 and 8 control for a village-level estimate of infant mortality, computed from mothers’ reported birth

> 27Household-level open defecation, used instead of village-level, explains 68 percent of the gap, a reminder of the importance of disease externalities.

> 28Jayachandran and Pande (2012) note that birth order is a predictor of child height in India (see footnote 10). For example, in the sample of rural children under 5 used here, I find that first children are 0.063 ( _p_ = 0 _._ 044) standard deviations taller than second children. However, this gap falls to 0.034 (
are summed to create the outcome variable. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
Figure 5: Projections of Secondary Enrollment <!-- Start of picture text --> 7000000 120.0%<br>6000000<br>100.0%<br>5000000<br>80.0%<br>4000000<br>60.0%<br>3000000<br>40.0%<br>2000000<br>20.0%<br>1000000<br>0 0.0%<br>2017 2018 2019 2020 2121 2022 2023 2024 2025<br>Lower secondary enrollment, GoT Lower secondary enrollment, WB<br>Lower secondary GER, GoT Lower secondary GER, WB<br><!-- End of picture text --> Source: 2017 Government of Tanzania and World Bank secondary enrollment projections.

These increases in enrollment have exceeded the rate at which the government is hiring teachers and building classrooms, leading to larger class sizes. In 2018, a primary school classroom was used by an average of 81 students - double the standard of 40 students per classroom (World Bank 2019b). In 2016/17, this was met with a non-commensurate 9 percent increase in the number of primary classrooms. This led to a higher primary stream/classroom ratio, which rose from 1.8 streams per classroom to 1.9.

Data from the 2016 Sauti za Wananchi survey corroborate these shortfalls - households with children that participated in this survey reported shortages of teachers (34 percent), lack of desks (30 percent) and lack of classrooms (13 percent) as the main challenges facing public schools in Tanzania (Sauti za Wananchi 2016). Across the system, estimates of teacher requirements show that there are only half of the needed secondary school mathematics teachers and only 75 percent of the required physics teachers (Figure 8, World Bank 2018b).

To maintain an average of 602 students per school, World Bank projections estimate that the system needs 7,000 new schools - an increase of 40 percent
The challenge is compounded by the fact that there is limited documentation of this policyrelevant topic in Sub-Saharan Africa, in part because there are no systematic teacher salary data across African countries. UNESCO occasionally documents teacher salaries in terms of country GDP per capita (e.g., UNESCO 2009),<sup>4</sup> but it does not provide information on relative earnings differentials. Understanding the structure of earnings in countries with similar economic structures may be one input into policymaker discussions about setting salaries.

In this study, we assemble data from representative population or labor force surveys across 15 Sub-Saharan African nations. We characterize the demographics of the teaching workforce, compare earnings from the primary job for teachers to other wage workers of similar age, education, rural/urban sector, and gender. We then explore structural factors that may explain teacher wage differentials.

Our analysis shows that, across countries, teacher earnings rise in absolute terms with GDP per capita but that they fall as a percentage of GDP per capita – i.e., they rise more slowly than GDP per capita. Within countries, we find that in seven out of the 15 countries, teachers’ monthly earnings are statistically significantly lower than that of other comparable wage workers. In five countries (Burkina Faso, Côte d’Ivoire, Namibia, Senegal and Zambia) teachers’ monthly earnings are statistically significantly higher than comparable workers. But simple earnings comparisons are complicated by adjustments for working hours. In four of the eight countries where teachers’ monthly earnings are lower and where teachers report their hours, their hourly wage is higher (albeit not statistically significantly so) than that of other workers. Positive premia tend to be higher for teachers on permanent contracts – who get paid more but work similar hours – than for teachers on fixed-term contracts. Teacher earnings are not more determined by age and education than those of other workers. The fraction of teachers in the workforce has also risen across countries over the last two decades. Contrary to expectations,
shrinks as permit prices drop.

To calibrate our model, we assume a fictitious market in the US for Mexican workers. We first estimate what earnings prospective migrants from Mexico would earn in the US by estimating Mincer-type regressions on US survey data and inferring migrant earnings using the characteristics (age, education, etc.) of a Mexican worker. We further deflate these earnings by applying a wage tax schedule and convert post-tax earnings to their real values by deflating with the prevailing relative price indices (Argente et al., 2023). Moreover, we 2