rafmacalaba/data-use-mentions-extended huggingface dataset:
**_Figure 1: Share of foreign workers in the respective employed populations (percent)_**

<!-- Start of picture text --> 30 20<br>25<br>15<br>20<br>15 10<br>10<br>5<br>5<br>0 0<br>Total employment Youth employment Employed men Employed women<br><!-- End of picture text -->

# _Source_ : LFS, various years.

Note: Youth refers to those 15 to 24-years-old.

**Indonesians make up the largest share of the foreign worker population, according to MOHA data.** Forty percent of foreign workers are Indonesian, followed by Bangladeshi (28 percent) and Nepalese (16 percent) (Figure 2). Anecdotal evidence suggests that Indonesians move to, and work in, Malaysia as irregular foreign workers more than other nationalities, benefiting from the close geographical proximity and Indonesian migrant networks in Malaysia, and therefore the Indonesian foreign worker share could be even larger than indicated by the MOHA data. Corroborating anecdotal evidence is that Indonesia has seen an influx of returning irregular overseas workers from Malaysia because of job losses during the ongoing COVID-19 crisis.<sup>‡</sup> Additional evidence is that the Indonesian government sent aid packages to support Indonesian day laborers in Malaysia—who are highly likely to be foreign workers—who were adversely affected by the Movement Control Order (MCO) implemented by the Malaysian government as a COVID-19 mitigation measure.

**Foreign workers are largely concentrated in physically demanding sectors of the economy such as manufacturing, construction and plantations** (Figure 3) **.** Foreign workers pick up the ‘3D’ (dirty, difficult, and dangerous) jobs in these sectors that Malaysians stay away from. In 2019, 35 percent of foreign workers were in manufacturing, 22 percent in construction, and 15 percent in services. Studies report, for instance, that the
for example, refrigerator, TV, and telephone); housing; assorted items such as clothing, furniture, medical, ceremonies, education (tuition, uniform, transportation, boarding); and others. Regarding the housing expenditure, the actual monthly rent paid was recorded. However, if the household owns the house, the estimated rent was imputed.

> 5 Temporal deflation is based on the consumer price index series; spatial deflator is calculated based on the ratio of the regional poverty lines to the national poverty line, obtained from the National Socioeconomic Survey (SUSENAS) of the corresponding wave.

> 6 See <u><mark>https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2015WR016954 f</mark></u> or more details on the computation method.

6
Variables marked with * are used both as reweighting target variables and as variables included in the SWIFT poverty projection model. To avoid repetition, we only include these variables in the first section (reweighting target variables) or in the first section where they show up (SWIFT model Variables-rural) but exclude them from subsequent sections (SWIFT model variables-urban). Source: authors’ estimation using EICV5 (2017).

# _III.2 Results from experiments with Saint Lucia HBS 2016 data_

For Saint Lucia’s experiment, the poverty estimate according to the official statistics is 25.0 percent, and the poverty rate among the subsample without any weight adjustment is 22.2 percent (Figure 3). The difference of 2.8 percentage points between the actual rates of the reference and phone surveys is not statistically significant at the five percent level. This small difference stems from widespread mobile phone ownership; in the Household Budget Survey 2016, 81.5 percent of households (1,214 out of 1,490) stated they own a mobile phone.

Like Rwanda EICV5 in the previous section, three types of weights were calculated to adjust for the sampling bias in the phone owners’ sample of HBS 2016 in Saint Lucia, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW. The first subfigure in Figure 3 compares the poverty rates of the phone owner sample by applying the abovementioned weights to the consumption data. The poverty rates among the phone owners applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 22.2%, 23.5%, 24.2%, and 24.7%, respectively. Among the last three poverty rates with adjusted weights, the performance of 15
mines are important for economic growth, artisanal mining continues to be important for employment.<sup>4</sup> Prospects for linkages and value additions of the mining sector, need to be utilized effectively. The 2013 World Development Report (World Bank, 2013) shows that extractive industries tend to have weak linkages, with dismal impact on employment of 1 to 2 percent of total workforce.

<!-- Start of picture text --> Figure 2<br>DRC: Terms-of-Trade Index<br>(2002: 100)<br>350.00<br>300.00<br>250.00<br>200.00<br>150.00<br>100.00<br>50.00<br>0.00<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Source: Author’s calculation based on World Bank Database.

The foregoing discussion demonstrates DRC’s dependence on natural resources, which means that price fluctuations of its exports can be major sources of economic volatility

> 4 According the World Bank (2008), artisanal mining production in DRC was estimated at 90 percent in 2008 and the number of persons directly and indirectly dependent on this activity was estimated at 8 to 10 million, about 14-16 percent of DRC’s population.

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human capital development. The enrollment rates have not been picking up fast and the future trend of human capital does not look optimistic; in fact it is claimed the slack private sector investments in Ghana might be a result of lack of skilled labor and lack of adequate human capital stock. The non-school attendance rates in Ghana are very high with gender disparities. 1992 GLSS data indicate that one in three girls and one in four boys do not attend school. The rural non-schooling is higher with 37 percent for girls and 28 percent for boys. "Ghana 2000" in its strategy for accelerated growth has argued for massive investment in primary education as a way of building necessary human capital for sustainable broad-based growth (World Bank, 1993). It is therefore important to understand the dynamics of household decision making as to whether to send children to school. and/or work, to benefit from investments in education. If not, colossal public investments in education are not likely to get children into class rooms.

It has been shown that poor households in developing countries seldom find it viable to send their children to school; they are also in need of cash income. Hence parents do not send their children to school and allow them to work. This issue has been discussed in the context of Ghana by Canagarajah and Coulombe (1997) using GLSS 34
2011; Kilic and Moylan 2016). However, differences in methodology across studies impede broader comparative analysis and limit the conclusions that can be drawn for developing countries at large.

This paper contributes to the emerging literature on gender gaps in property ownership using nationally representative data collected by the Demographic and Health Survey (DHS) program. Recent rounds of the DHS project (starting in 2010) collect data on ownership of land and housing from adult men and women. The DHS is particularly suited for gender analysis of property ownership as eligible respondents are personally asked about their ownership of land and housing property, instead of relying on proxy information given by other household members. Doss et al (2015) use the DHS data to provide estimates of land ownership by women and men for eight countries in Africa. Gaddis et al (2018) expand this analysis to cover 28 African countries.

This research, to the best of our knowledge, is the first to take a global perspective on gender gaps in the ownership of property. Its main contribution is twofold. First, using data from 41 countries across the world, we document the extent of gender gaps in property ownership worldwide and explore descriptive patterns within and across countries. Second, we use multivariate analysis to assess in greater detail what factors are correlated with the gender gap in property ownership, with a particular focus on the role of legal systems. We find that there is substantial variation in gender gaps across countries, but in almost all countries men are more likely to own property than women. Within countries, gender gaps in property ownership are larger for the more disadvantaged population groups – that is, they are larger in rural than in urban areas, and larger for the poorest than for the richest wealth quintiles. But what drives these gender gaps? While the analysis in this paper cannot establish causality, the multivariate analysis shows that women’s disadvantage in property ownership is associated with discriminatory norms and laws 2
specific measures of de-licensing and FDI reform.<sup>21</sup> We do not take into account the labor market reform, most of which occurred before the first year of our sample 1993 (Ahsan and Pagés, 2009).

To capture the effects of the de-licensing reforms, we use information from Harrison et al. (2011), who extended the data used by Aghion et al. (2008) to 2004, on the basis of Press Notes from the Ministry of Commerce and Industry. The de-licensing variable is a dummy that takes on a value of one if any products in a 3-digit industry have been de-licensed, and zero otherwise. Similarly, the measure of FDI reform was compiled by Harrison et al. (2011) also based on Press Notes from the Ministry of Commerce and Industry. It takes on a value of one if any products in a 3-digit industry have been liberalized, and zero otherwise.<sup>22</sup> In Table 6, we present the results from the modified specification. We find a positive correlation between de-licensing and FDI reform and firm productivity. More importantly for the purposes of this paper, our results on services reform are barely affected by this change.<sup>23</sup>

# **_Instrumenting the Services Liberalization Index_**

In order to ensure that our finding of services reforms improving manufacturing performance is not driven by reverse causality, we instrument the index of services reform. The intuition behind our instrumental variables (IV) approach is that India will react to services liberalization undertaken by other countries, especially economies it views as its competitors, such as China and Indonesia. We measure services liberalization using the WTO commitments in a given sector. More specifically, we focus on the number of commitments made by a country expressed as a percentage of possible commitments. For the years prior to the first full year of the WTO membership of a given country (e.g. 2002 for China), the number of commitments equals
the resulting point and noise estimates. CMN (2020) also present evidence on the fact that, even if the location effect was modeled at the domain level and bootstrap samples of clusters are taken, some bias would still likely remain in the resulting estimates of the original implementation of EB in `PovMap` and the `sae` Stata package.

The Stata package for small area estimation developed by Nguyen et al. (2018) has been updated to integrate the fitting methods from Van der Weide (2014) with the prediction and bootstrap methods from MR (2010). The new method is referred to as the H3-CensusEB (Molina, 2019), and the previous method (i.e. original bootstrap using EB from Van der Weide (2014)) is called H3-CBEB.<sup>5</sup> CMN (2020) performs a model-based validation of the different methods: i) CensusEB, ii) EB, iii) H3-CBEB and iv) ELL. CMN (2020) extend the simulations done by MR (2010) by i) including the area means of the covariates as additional variables in the model; ii) considering a model that has considerably higher explanatory power by adding more covariates; iii) considering larger population sizes and smaller sampling fractions; and iv) generating errors from a Student’s _t_ 5 instead of a normal distribution. However, in all these simulations, population data are generated under model (1).

Currently, the software available for small area estimation of non-linear parameters, such as the `sae` Stata package by Nguyen et al. (2018) as well as the R package `sae` by Molina and Marhuenda (2015), only allow for estimation under the nested error model specified in (1). However, since household surveys often use two stage sampling, it seems appropriate to consider a twofold nested error model. Marhuenda et al. (2017) extend the EB method from MR (2010) to a twofold nested error model,
electricity access and a decrease in the reported occurrence of regular blackouts. At the same time, the insignificant coefficients of the interaction term in columns (4) to (6) suggest that the project did not cause households who were living in project areas to pay more for their electricity.

According to the results in columns (1) to (3), the change in the share of households with access to electricity increased by 5.7 to 5.9 percentage points more in areas with project components than in areas without project components. The coefficient (0.057) of the interaction term ( _Post × Treat)_ in column (1) is statistically significant at the 1-percent significance level. Once the control variables are added in column (2), the coefficient increases to 0.059 and remains highly significant. The result is also robust to the inclusion of district fixed effects (as reported in column 3) and to clustering standard errors at the district or stratum level (not reported here).<sup>12</sup>

> 12 In the LSMS dataset, stratum is defined based on region and a dummy for rural areas. There are three regions in Malawi (North, Center, South) so that stratum takes six different values.

10
In Table 4 we first regress _Access to loan_ on _CB reform_ and find that the introduction of a CB is associated with an increase in access to finance. These regressions are estimated using a linear probability model (LPM) and standard errors are clustered at the survey level. Specifically, controlling for country and year fixed effects,<sup>23</sup> in the years after which a CB is introduced in the country, there is a 6 percentage point increase in the average firm’s access to a loan in the overall sample. We find similar point estimates in the MSME and SME sample. The association between CB reform and access to finance is not statistically significant in the large firm sample as seen in column 4 of Table 4. While we expect MSMEs and SMEs, which are potentially the most financially constrained firms in an economy, to benefit the most from reforms that improve the credit information environment in the country, we are not able to reject the null hypothesis for the equality of the estimates for MSMEs and SMEs against large firms.

Having established the effect of CB reform on access to finance, we next re-estimate equation (2) but replace _Access to loan_ with the more exogenous _CB reform_ . Using the Enterprise Survey data, Table 5 shows that the introduction of CBs leads to 1.28 percentage point increase in employment growth for the overall sample. MSMEs and SMEs again respond more to the supply shock (1.34% and 1.79% increase in employment growth respectively), while the larger firms have a lower increase in employment growth (1.11%) that is not statistically significant. Statistically, we confirm using t-tests that the estimates for MSMEs and SMEs are higher than those for large firms at the 99% confidence level.

In Table 6, we confirm the impact of CB reform on employment growth using the ORBIS sample but the magnitudes we find are larger. Introduction of CBs leads to an over 5 percentage point increase in employment growth both for the overall
<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0<br>English Spanish French<br>Google Trend Index<br><!-- End of picture text --> Note: It shows the trend for the expression “online shopping” in English, Spanish or Portuguese, and French.

Figure 1: Google trend for the expression “online shopping” This wave of digitalization comes, however, with an important risk; the deepening of the digital divide. Literature has shows that significant differences in the use of digital technologies across firms existed pre-pandemic (Cirera et al. (2022)). These can be explained by several factors such as access to infrastructure and finance or capabilities to successfully implement these technologies. While the shock on digital demand for businesses can offer an opportunity for smaller firms and those less exposed to digital technologies to catch up, it can also lead to an increasing gap, if more capable firms are disproportionately able to respond faster to this shock. For example, in retail and wholesale, the shock may lead to an increasing market share towards well-established e-commerce platforms.

The objective of this paper is to describe the use of digital technologies during the pandemic and the heterogeneity in its use for 57 (mostly developing) countries, where the evidence is very scarce. Specifically, the paper analyzes the gap in digital responses to COVID-19 across businesses. It relies on three waves of the World Bank’s Business Pulse Surveys (BPS) and COVID-19 Followup Enterprise Surveys (WBES), which provide comprehensive information on the impact of the COVID-19 pandemic on businesses worldwide, including digital adjustments made by firms in response to the shock.<sup>2</sup> The paper aim to address three key questions: i) What is the share of firms that started or increased the use of digital technologies in response to COVID-19 and for what business purpose
Policy Research Working Paper 9272

# **Abstract**

This paper reports on electronically-monitored improved use of the “Mirt” biomass stove in Ethiopia over a relatively long period of three-and-a-half years, using stove use data collected at five points in time. The results show that 62 percent of the households surveyed still retained their stoves after more than three years, which is a low level of abandonment, as the lifetime of the Mirt stove is approximately five years. Dis-adoption of the stove is not correlated with any of three monetary incentives provided at the time of distribution. With and without adjusting for dis-adoption, no longer-run differences in stove retention are found across treatments. Among those who retained their stoves, average regular stove use increased over time, but generally it is statistically the same toward the end of the first year. Thus, despite the relatively long timeframe, no decline is observed in regular usage. Comparing the persistence of the treatment effects, the paper finds that, in the longer run, subsidizing the cost most effectively promotes increased regular use over time.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mtoman@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
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data underreporting by firms, formulated in terms of the typical behavior of a firm in the same area of activity. The fact that these studies often report a sizeable degree of underreporting together with intuitive correlations with observable firm and investment climate characteristics suggests that these self-reported measures capture underreporting to some degree. However, it remains unclear how reliable these measures are without further probing the underlying assumption that firms report truthfully about untruthful reporting (sic).<sup>5</sup> Firm-level studies which do not specifically focus on informality and/or misreporting almost always assume that firms report truthfully or that firm-level measures suffer from classical measurement error only. However, to the extent that misreporting behavior is systematically related with (observable and/or unobservable) firm-level and investment climate characteristics for which the analysis does not control adequately, the reported results will suffer from systematic (and unknown) measurement error bias. Also if one relies on survey rather than tax office data in the analysis, it is not clear to which extent the survey data suffer less from misreporting than tax office data.

Using unique firm-level survey data matched with official tax data, we attempt to estimate the unobserved true sales and the underreporting in sales to the tax office of formal sector firms in Mongolia. Based on the existing approaches used in the shadow economy literature, we can distinguish among three possible ways of estimating this underreporting.<sup>6</sup> 5 In this respect it is interesting to note that at the time the initial RPED surveys were planned serious doubts were raised whether reliable data could be generated at all with structured questionnaires in a large-scale survey, especially in developing countries. 6 Schneider and Enste (2000) provide a comprehensive review of the three approaches.

3
J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and 43 Productivity Zhang, Q; Seto, K.C (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. _Remote Sensing of Environment_ , 115(9): 2320– 2329. https://doi.org/10.1016/j.rse.2011.04.032 Zhou, Y; Smith, S. J; Zhao, K; Imhoff, M; Thomson, A; Bond-Lamberty, B; Elvidge, CD (2015). A global map of urban extent from nightlights. _Environmental Research Letters_ , 10(5): 54011. <u>https://doi.org/10.1088/1748-9326/10/5/054011</u> Zhou, N; Hubacek, K; Roberts, M (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. _Applied Geography_ , 63: 292–303. https://doi.org/10.1016/j.apgeog.2015.06.016
Figure 6: Distribution of Mean Daily Temperature <!-- Start of picture text --> Ethiopia (n = 10,674) Malawi (n = 8,897)<br>.2 .4<br>.15 .3<br>.1 .2<br>.05 .1<br>0 0<br>10 15 20 25 30 35 20 22 24 26 28 30<br>Niger (n = 3,913) Nigeria (n = 9,145)<br>.6 .8<br>.6<br>.4<br>.4<br>.2<br>.2<br>0 0<br>28 30 32 34 36 20 25 30<br>Tanzania (n = 9,916) Uganda (n = 11,692)<br>.25 .4<br>.2<br>.3<br>.15<br>.2<br>.1<br>.1<br>.05<br>0 0<br>15 20 25 30 18 20 22 24 26 28<br>Mean Seasonal Temperature (C) Mean Seasonal Temperature (C)<br>CPC ERA5 MERRA-2<br>Density<br>Density<br>37<br>Density<br><!-- End of picture text --> _Note_ : The figure presents the distribution of mean daily temperature pooled across all years, disaggregated by Earth observation source. Each distribution (Earth observation product) in each panel is constructed using all household-year observations ( _n_ ). Variation in distributions do not come from variation in the household data that is paired with the Earth observation data. Rather, variation in distributions within a panel is solely due to differences in the temperature reported by the Earth observation source.