rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Making Time Count**

**A Machine Learning Approach to Predict Time Use In Low-Income Countries**

# **from Physical Activity Tracking Data**

Joris Mulder<sup>†</sup> , Seyit Hocuk<sup>+</sup> , Talip Kilic*<sup>‡</sup> , Alberto Zezza<sup>‡</sup> , and Pradeep Kumar<sup>†1</sup>

**JEL Codes:** C63, C83, J22.

**<mark>Keywords</mark>** <mark>: time use, accelerometer data, activity recognition, predictive modeling, machine learning, classification models.</mark>

1 * Corresponding author: tkilic@worldbank.org. ‡ Development Data Group, World Bank; † Centerdata, the Netherlands; + Fabory, The Netherlands. The order in which the author names appear has been randomized. This paper was produced with the financial support from the World Bank Living Standards Measurement Study (LSMS), the World Bank Research Support Budget (RSB), and the 50x2030 Initiative to Close the Agricultural Data Gap, which is a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030.
have now been used for services exports. It is plausible that the value of exports of goods can be approximated using the shares of the number of employees in the same sector within a specific region. In addition to the shares on employees, shares relating to the number of companies with more than ten employees operating within the electricity sector were also used. In this way, a double re-proportioning was carried out to obtain the final data;

- Export of services: data from 2016 from the Bank of Italy are used for the export of services. They refer to “ _Crediti per esportazioni di servizi per tipologia e ripartizione territoriale_ ” (i.e., credits on exports). The services reported by the Bank of Italy are grouped as for import of services. The unique difference is that data are regionalized using shares calculated on the number of employees. We used the same procedure for the transport sector as used for the import of services.

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that criterion, the most well-off beneficiaries<sup>were found in the Western Cape, followed by</sup> 7As there is no official poverty line in South Africa,<sup>we use the 1993 household subsistence line calculated by the Institute for</sup> Planning Research at the University of Port Elizabeth.<sup>The figure has been adjusted using an adult equivalence scale, and made</sup> comparable to 1999 figures from the survey using the<sup>South African Consumer Price Index provided by Statistics South Africa.</sup> 10
# **6 Concluding Remarks**

Mobile phones have become ubiquitous. It has revolutionized telecommunication and offers an array of services such as mobile banking, and access to social media. Mobile broadband Internet also offers millions of people access to the Internet for productive uses and leisure. In this paper, I present global evidence on the impact of mobile phone access on local economic development. To do this, I assemble a unique granular dataset on the global expansion in 2G, 3G and 4G mobile networks spatially matched with satellite data on nighttime lights.

The findings of the paper show that access to mobile phones is a significant driver of economic activities. The results provide suggestive evidence that the prior evidence in the literature on the effects of mobile phones and mobile broadband Internet on households and firms translates into meaningful impact on economic development. Moreover, the effects of mobile network penetration are more pronounced in developing countries as well as those that hitherto had low access to fixed-line telephone network infrastructure.

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# **Table 2. Pre-Treatment Summary Statistics**

The sample consists of all nonfinancial listed firms in the CSMAR database. The table presents variable average and standard deviation (in the bracket below) for the entire sample, as well as a subsample of firms that receive land titles in the land titling program ( _Titling firms_ ) and firms that do not ( _Non Titling firms_ ). The difference between the values for Titling firms and Non Titling firms are presented in Column (4). The statistics are calculated based on the financial reports of firms **in 2011** , the last year before the title reform was implemented in 2012. The t- statistics in brackets of column (4) are for the null hypothesis that there is no difference between _Titling firms_ and _Non-Titling firms_ . *,**,*** indicates significance at the 1%, 5% and 10% level respectively.

||Whole Sample|Titling<br>firm|Non-titling<br>firm|Difference|
|---|---|---|---|---|
||N=1250|N=43|N=1207||
|Sales (Billion RMB)|5.315|5.317|5.266|-0.055|
||(8.237)|(8.231)|(8.507)|(-0.043)|
|Total asset (Billion RMB)|8.342|8.323|8.879|0.576|
||(11.017)|(10.998)|(11.650)|(0.338)|
|Employment (Thousand)|4.576|4.569|4.786|0.206|
||(5.708)|(5.668)|(6.803)|(0.232)|
|Book leverage ratio|
argued that they are given by social norms that limit females’ outside labor market participation, while men are engaged in energy-intensive labor market activities. Thus, few women are engaged in plowing in India, while in Bangladesh no women are observed to pull rickshaw<sup>10</sup> . As a consequence of gender-segregated occupations, as PRH demonstrated, health endowment in the labor market matters only for males. Behrman and Deolalikar (1989) and Sahn and Alderman (1988) also find that health and calorie consumption have significant positive effects on men’s but not on women’s wage rates. So for females, I estimate the technology without differentiating them based on the energy-intensity of their occupation. Moreover, consistent with PRH assumption, there are only a very few females engaged in high energy intensive occupations in the data (see Section 5). Thus, for each of these three categories, I estimate the following health technology function using IFE: where household characteristic vector, **Xhjst** , includes monthly per capita expenditure and its square, per capita household landholding, and household size, all in logs, share of different demographic composition of the household, _Rt_ are survey round dummies to control for any potential seasonal effects on health outcomes, and ∆indicates deviation of an individual’s observation in a given round from its mean (over four rounds). In estimating equation 9 for females, I also include pregnancy and lactating dummies.

Applying IFE estimates of the parameters for calorie intake, age and its square, and household characteristics from equation 9 to the individual data of the corresponding variables, I obtain ˆ estimated log weight for height ( _ln_ ( _h_ )) for each individual who belongs to any of the three above sex-occupation categories. Deducting this estimated value from the observed value of log weight for height ( _lnh_ ) yields a health measurement that includes an individual’s unobserved health endowment ( _μ_ ) and an aggregate unmeasured effect of time-invariant household characteristics ( _ρ_ ) (e.
_Australian Journal of Agricultural & Resource Economics_ 67 (2): 285-302. Chiovelli, G., S. Michalopoulos and E. Papaioannou. 2018. "Landmines and Spatial Development." National Bureau of Economic Research, Inc, NBER Working Papers: 24758. Chiovelli, G., S. Michalopoulos, E. Papaioannou and S. Sequeira. 2021. "Forced Displacement and Human Capital: Evidence from Separated Siblings." National Bureau of Economic Research, Inc, NBER Working Papers: 29589. Couttenier, M., N. Monnet and L. Piemontese. 2022. "The Economic Costs of Conflict: A Production Network Approach." Centre for Economic Policy Research, Discussion Paper DP16984, London. Deines, J. M., et al. 2021. "A Million Kernels of Truth: Insights into Scalable Satellite Maize Yield Mapping and Yield Gap Analysis from an Extensive Ground Dataset in the Us Corn Belt." _Remote Sensing of Environment_ 253: 112174. Deininger, K., et al. 2023a. "Quantifying War-Induced Crop Losses in Ukraine in near Real Time to Strengthen Local and Global Food Security." _Food Policy_ 115: 102418. Deininger, K., et al. 2024. "Micro Level Impacts of the War on Ukraine’s Agricultural Sector: Distinguishing Local and National Effects over Time." _World Bank Policy Research Working Paper_ , Washington, DC. Deininger, K. W., D. A. Ali and R. Neyter. 2023b. "Impacts of a Mandatory Shift to Decentralized Online Auctions on Revenue from Public Land Leases in Ukraine." _Journal of Economic Behavior & Organization_ 213: 432-450. Devadoss, S. and W. Ridley. 2024. "Impacts of the Russian Invasion of Ukraine on the Global Wheat Market." _World Development_ 173: 106396. Duncan, E. C., S. Skakun, A. Kariryaa and A. V. Prishchepov. 2023. "Detection and Mapping of Artillery Craters with Very High Spatial Resolution Satellite Imagery
in the past have proved extremely useful learning mechanisms.<sup>2 9</sup> The Kanagaratnam Mission to Kenya is one recent relevant example.

# Inhibiting Factors

- Frequent changes in the mission leadership or the top program management.

- Lack of follow-up mechanisms to learn about sustainability of project benefits after the project is completed.

- Massive base-line, mid-project and end-project field surveys which take too long to provide meaningful feedback at the right time. There is a need to simplify and shorten the methodology, instruments and analytic procedures for such surveys to ensure selective but timely feedback. Ocher methods of research such as case research, action research, focus group discussions, brainstorming, joint sessions with beneficiaries, etc., can also provide less rigorous but useful instruments of organizational learning.

- Project designs which are not flexible enough to allow midcourse correctiQns.

- Limited supervision time and tight visit schedules which do not allow open-ended wandering around to learn from the project implementation experience.

- Lack of broad-based participation in project formulation and evaluation from different levels of administration and different agencies. The PPAR for Egypt-I and the PCR for India-I mention inadequate involvement of country counterparts in project formulation. Even where there is an attempt to involve program managers, the project dialogue is confined to one or two ministries and that too only with top management. Even if the next project is in another location or another country, it would help to include one of the more perceptive members of the first project team in the formulation mission. For example it would be useful to have an Indonesian program manager attached to an Indian mission or vice versa.

- Lack of involvement of beneficiaries in project formulation process. The project design is not adequately influenced by client needs or the nature of worker-client transactions. A recent review of the community participation component in Bank projects concludes that some of the successful PHN

29Lauren Chester, "Monitoring and Evaluation Components in Completed Population Projects," April 1985, pp. 14-15.

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# II. <u>Findings based on data from host governments</u>

To obtain a more realistic picture of what is happening on the ground, the study made an effort to collect data from host country governments. Host governments tend to capture the data more comprehensively and accurately than the home government, simply because most host countries subject all FDI projects, large or small, to government approvals or registries at the point of business entry.

Collecting data from African host countries has not been easy. Although all countries gather general FDI data, data specifying firms’ origins, overtime changes, sector distributions and economic impact are not always readily available. Moreover, the quality of data is very uneven and generally poor. Many agencies in a country may be involved in FDI statistics; each has only a limited view of the whole picture. For instance, investment agencies may be charged giving initial project approvals, company registries responsible for legal establishment of the businesses, and central banks track the actual capital flows. In addition, other line ministries may have a responsibility to track the FDI impact on economic sectors, job creation, exports and tax revenues generation. Ideally, data from all sources are needed to reflect FDI from its intentional initiation, through actualization, and to economic impact generation, but this would require sophisticated FDI data systems which few African countries have at present.

To gain at least a preliminary understanding, the research started with a mini-survey among the national investment promotion agencies (IPAs). Targeting IPAs as the starting point makes sense where they are mandated as the “One Stop Shop” for FDI promotion and facilitation. These IPAs often do their best to gather FDI statistics through coordination with other relevant government agencies even if not directly being in charge of all FDI aspects.

Six national IPAs participated actively in the survey and provided extremely useful data as requested. These included four of the top five African recipients of Chinese investment according to MOC statistics: Nigeria, Zambia, Ethiopia and Ghana.<sup>
445|
|Peninsular Bumiputera|0.512|0.432|0.553|0.531|0.345|0.543|0.550|0.092|0.480|
|Peninsular non-Bumiputera|0.295|0.217|0.160|0.278|0.181|0.149|0.278|0.034|0.091|
|East MY Bumiputera|0.151|0.692|0.261|0.154|0.612|0.279|0.131|0.298|0.372|
|East MY non-Bumiputera|0.042|0.247|0.026|0.037|0.272|0.030|0.041|0.143|0.056|

* Labuan, Sabah and Sarawak; ** Kelantan, Pahang and Terengganu. Source: Authors’ calculations based on DOSM data. Note: All figures are aggregated from household-level data using sampling and

population weights. Mean and median income are in terms of gross monthly household income per capita in 2016 Malaysian ringgit. The poverty line is defined as the 40th percentile of household income per capita in 2004, i.e., MYR 527 per month in 2016 prices. The poverty figures by subgroup give, per survey round: how large the group is as a proportion of the overall population (first column), the headcount ratio within the subgroup (second column), and what share of the total number of poor this represents (third column).

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between the two bank types. This allows two alternative conclusions, which our data do not allow to distinguish: off-setting effects of Sharia-compliant banking on business model, risk taking and ultimately stability cancel each other out, or the functioning and organization of Islamic banks is indeed less different from that of conventional banks than often propagated.

This being one of the first bank-level explorations of Islamic banks, two important caveats are in place. First, anecdotal evidence suggests that there are significant differences across countries in terms of how Sharia-compliant products are exactly structured, with some of the banks basically offering conventional products repackaged as Sharia-compliant products. This implies that we need to exercise caution when interpreting Islamic banking in the context of traditional models of financial intermediation. In addition, there are differences across different Muslim countries in what is considered Sharia-compliant and what is not, which makes it difficult to do cross-country comparisons. Second, given the different nature of conventional and Sharia-compliant products, as discussed in section 2, balance sheet and income statement items might not be completely comparable across bank types even within the same country. In our empirical exercise, we rely on Bankscope data that have been subjected to consistency checks by the provider VanDyck. However, we cannot exclude the possibility that significant differences in ratios derived from financial statements are due to different measurement issues rather than inherent differences across bank types. Finally, our sample includes relatively few Islamic banks which might bias our findings and can only be remedied over time as more data become available.

The remainder of the paper is structured as follows. Section 2 presents some of the basic Sharia-compliant products and links these products to the theoretical literature on financial intermediation. Section 3 presents data and methodology. Section 4 uses bank-level data to assess the relative business orientation, efficiency, asset quality and stability of Islamic and conventional banks. Section 5 compares the relative performance of conventional and Islamic banks during the recent crisis and section 6 concludes.

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**Table 2: Dropout Rates by Income and Ability.**

|||Abi|lity qui|ntile|||
|---|---|---|---|---|---|---|
||1|2|3|4|5|Total|
|5+ MW|81.36|65.83|61.48|52.13|39.04|44.73|
|3-5 MW|74.23|69.44|62.21|57.86|43.77|51.33|
|2-3 MW|68.54|67.58|63.73|57.68|46.53|55.09|
|1-2 MW|71.59|66.64|62.21|57.66|50.56|57.82|
|_<_1 MW|69.04|67.95|61.34|55.94|50.30|58.71|
|Total|70.99|67.44|62.37|56.96|45.84|54.36|

_Source_ : Calculations based on SPADIES for students from the 2006 entry cohort (first semester). _Notes_ : Each cell reports percent of students from a given income bracket and ability quintile who drop out of their bachelor’s program. A student is classified as a dropout if she does not graduate within eight years. Income is reported in brackets; MW = monthly minimum wage. Ability is reported in quintiles of standardized Saber 11 scores. Quintile 1 is the lowest.

early as in year 1— is a powerful predictor of final outcomes. This point is further illustrated in panel b, which classifies students into tiers by the end of year 1. Consistent with panel a, it shows the strong predictive power of early performance—as explained by the next stylized
explicit in collated records as part of disaster inventories but limit the comparability of data across different events and contexts.<sup>37</sup> Within the same survey, information collected in microdata is typically harmonized and comparable across events covered. While microdata are subject to differences in survey and questionnaire design across contexts, such differences are usually explicit. Analysts can therefore take these differences into account when making comparisons and there is scope for harmonization across contexts as in the case of the LSMS-ISA suite of surveys.

# **Geographical differences**

Many of the previously discussed sources of inaccuracies in estimates of disaster incidence and severity are more pronounced in low- and middle-income countries (LMICs).<sup>21,30,37,42</sup> This goes in particular for data from disaster inventories that depend on the comprehensiveness of coverage in the underlying data sources they draw on. For example, the density of information on shocks and natural disasters is lower in LMICs meaning that shocks are more likely to go unreported, information is more likely to be incomplete or inaccurate, poor and marginalized population groups are more likely not to be covered, and the impact on them is more likely to be underestimated due to a higher share of uninsured damages and a lower value of affected assets. On the side of microdata, surveys in high-income countries may provide more accurate data due to the availability of more sophisticated measurement approaches and higher levels of education among respondents which may positively affect the accuracy of self-reported information.

|**Dimension**|**Disaster Inventories**|**Microdata**|
|---|---|---|
||**Coverage**||
|**Shock coverage**<br>(Threshold and hazard<br>bias)|• Subject to reporting in aggregate data<br>sources and reports<br>• Salient shocks likely covered but data<br>less sensitive to idiosyncratic or small<br
**Table 4.** Distribution of employment elasticities in South Asia.

|**South Asia**|**Elasticity**|
|---|---|
|25<sup>th</sup> percentile|0.10|
|Median|0.20|
|75<sup>th</sup> percentile|0.60|
|Mean|0.34|
|Long-run|0.19|

**Source:** All comparable surveys highlighted in Table 1.

We report employment elasticities by country, sector of activity and type of job in Table 5, while also providing a breakdown by gender. Except for Bhutan, faster growth in South Asian countries has translated into more employment. The employment elasticity has been very low in India, driven by a negative elasticity in the case of female employment. Conversely, the elasticity is high in Pakistan, driven by a positive elasticity for female employment. However, the sectoral breakdown shows that in the case of Pakistan, the growth elasticity of female employment in agriculture is huge, whereas it is negative in India.

For most South Asian countries, the employment elasticity was strongest for the manufacturing sector. While jobs created in the manufacturing sector benefitted mainly male workers, the opposite was true in the services sector. In Sri Lanka, for example, the employment elasticity of the services sector was zero for men, but 0.5 for women. The elasticity of regular wage employment was especially high in Bangladesh, which can partly be explained by the large growth of manufacturing employment in urban areas (Farole and Cho 2017). In all countries the elasticity of regular wage employment is higher for women than for men.

**Table 5.** Employment elasticities by sector of activity and type of job.

|**C**|**Y**|**S**||**El**||**Indu**|*
access to electricity except for the percentage of households headed by men. This similarity in characteristics lends support to the idea of the treatment and control groups having similar trends in the absence of the oil.

# **5. Empirical Results**

# _5.1 Effects on Income and Consumption_

The results from estimating the impacts on real income and consumption are presented in Table 3.<sup>15</sup> The coefficient of interest is T*Post interaction, which is the effect of the oil discovery on outcome variables of interest. All specifications include district fixed effects and household controls. The coefficient on the log of real income is positive and significant. Household income increased by 39 log points (or about 47 percent) for the households in the oil region compared to the households in non-oil regions. Given the large magnitude of the effect, we investigate why households in the oil region experienced large changes in income. Our results indicate increases in the average employment income per household<sup>16</sup> at both the extensive and the intensive margin. First, the number of income earners per household increased by about 0.2 for households in the treatment group (in Appendix Table A-6). Given the average number of income earners in the pre-treatment period, this effect translates to about a 10 percent increase in the number of income earners. Second, we find an increase in household employment income by more than 30% (in Appendix Table A-7). This implies that employment income per income earner also increased.

In contrast, the effect on consumption (column 2) is statistically insignificant. We also investigate the effect on household poverty status by using a dummy variable P P , which is equal to one if a household is below the poverty line and zero

> 15 Data on price index used to compute the real income in this study is compiled by Ghana Statistical Service (GSS). This data is available in the 3 rounds of GLSS data.

> 16 Employment income includes
which will require over 4 million km of transmission lines and over 4 TW of substation capacity to be built _each year_ over this decade and the number will increase to over 6 million km and 6 TW, over the next decade (Figure 2). This will need to be accompanied by rapid digitalization of the networks that stand at less than 20% and will need to be tripled according to IEA. It is hard to put a cost estimate on the global effort. However, if we attach a notional 0.5 million per km on average,<sup>5</sup> it amounts to $2 trillion to $3 trillion per year over the next three decades. Again, this is going to be a fraction of what is needed for the generation component but marks a phenomenal increase over the current state of play in network investments.

> 4 Analysis by one of the state regulators among others over the past few years has pointed to a cost impost of Rs

> 1.2/kWh (or 1.5 c/kWh) from renewables mostly linked to transmission (Singh, 2020).

> 5 This represents a mid-tier 400-500 kV new line all-in cost estimate using US DOE statistics.

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