rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 2. Effect of the 2017 tax-benefit system, by types of households, age groups and locations, change in consumable income, %**

**Figure** 3 **. Effect of the 2017 tax-benefit system, by deciles of original income** , change in consumable income, %

<!-- Start of picture text --> 100<br>50<br>30<br>10 50<br>-10<br>-30<br>-50 0<br>-50<br>1 2 3 4 5 6 7 8 9 10 Total<br>direct taxes and SIC pensions<br>direct taxes and SIC pensions other transfers indirect taxes<br>other transfers indirect taxes total<br>total<br>Source: Authors’ calculations using RUSMOD for 2017<br>Source: Authors’ calculations using RUSMOD for 2017<br>policy year and 2016 RLMS-HSE data.<br><!-- End of picture text --> Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

The fiscal system redistributes income from the upper to the lower part of the income distribution. When ranked by the original income, the bottom five deciles are net beneficiaries, while the top five deciles are net contributors to the system (Figure 3). At the bottom, government transfers (pensions in particular) are the main sources of income. The flat rate PIT limits the redistribution beyond a certain point: the richer seventh to tenth deciles appear to pay a similar share of income in direct taxes and social insurance contributions. The indirect taxation (VAT and excises) is slightly regressive, particularly beyond the fourth decile.

Finally, a preliminary analysis reveals that people in rural areas are net beneficiaries of the tax-benefit system in Russia, although the effect is small. One of the standard objectives of a fiscal policy is to help equalize living standards across the country. In principle, RUSMOD
### **2.2.3 Limited-Horizon Spectral Identification**

One criticism that can be leveled against the spectral identification approach is that the long-run VAR representation used to calculate the spectrum of the endogenous variables may be biased when estimated on a short sample of data. In practice, a ‘windowed’ selection of autocorrelations is often used to estimate the spectrum of a series. The same principle can be used in the spectral VAR identification process. For example, the infinite-MA representation, _D_ ( _e_<sup>_iω_</sup> ) can be replaced with a limited horizon series of impulse response coefficients.

One proposal is to use coefficients extending to 10 years of data, as in the original Max-Share approach:

# **3 Model Evaluation**

Traditionally, SVARs seeking to extract an unobservable shock, such as technology, have been evaluated on their performance using simulated data from a DSGE model (Barsky and Sims (2011), Francis et al. (2014), Chari et al. (2009), Erceg et al. (2005)). This is an intuitive evaluation, given the true underlying shock is known by construction. In this section, we argue that many of these tests have been performed using DSGE models which fail to test for the influence of material confounding shocks needed to replicate key features of US macroeconomic data. We later show that existing methodologies perform poorly when these features are included in simulated data from a simple 2-variable model that provides greater transparency over the data-generating process compared to a DSGE model.

## **3.1 DSGE Monte Carlo simulations**

Each VAR specification is tested on a standard medium-scale New Keynesian model, of the type proposed by Christiano et al. (2005), and used by Francis et al. (2014) and Barsky and Sims (2011) to evaluate their respective implementations of the Max-Share identifications.<sup>9</sup> This model contains features such as persistent consumption habits, investment adjustment costs,
**Figure 1: Informal competition and employment growth**

<!-- Start of picture text --> Figure 1A<br>0.60 0.50<br>0.40<br>0.20<br>0.00<br>Low informal competition High informal competition<br>-0.20 (residuals; roughly below median) (residuals; roughly above median)<br>-0.40<br>-0.60<br>-0.80<br>-0.74<br>-1.00<br>Figure 2A<br>0.6<br>0.45<br>0.4<br>0.2<br>0<br>Low informal competition High informal competition<br>-0.2<br>(residuals; roughly below 75th (residuals; roughly above 75th<br>-0.4 percentile values) percentile values)<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.4 -1.28<br>Employment growth rate (log<br>differences, %, residuals, mean values)<br>mean values)<br>Employment growth rate (%, residuals,<br><!-- End of picture text --> Source: Author’s own calculations based on Enterprise Surveys data.

Note: 1) Employment growth rate is calculated as the log difference in the level of employment (log of final year employment minus log of initial year employment) divided by the number of years between the initial and last year and multiplied by 100 to convert to percentage terms. The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against the residuals of informal competition (X-axis). The residuals are obtained by regressing the
features to train their machine learning models. High frequency data may aid in predicting some activities more reliably. The study by Staudenmayer et al. (2009) showed that high frequency data can help in better predicting repetitive activities like cycling in free living conditions.

Regarding the time use data, the interviewers were trained to elicit a narrative of each participant’s reference day, establishing first when the participant woke up and went to bed, and then recording the type and timing of the activities in between, in accordance with the respondent’s recollection. The time use module allowed for the specification of both main and secondary activity for each 15-minute interval. This feature of the module aimed at capturing simultaneous activities.

Having said that, recall-based time use reports are prone to potential errors, such as bad recollection of the performed activity or attributing the activity to the wrong time interval (e.g., Bell et al., 2019). This way of labeling the data also has the caveat that simultaneous activities can get mixed (Seymour et al., 2020). Especially, for short duration activities there is a possibility that more than one activity can be performed within the 15-minute time window. Hence, overlap in the activities can lead to mislabeling the physical activity sensor signal data but also to rounding errors of the time frames in which different activities are performed.

Furthermore, the activity list for the time use module does not precisely identify tasks or activity levels but are meant to provide a broader classification of how people spend their time. For example, “Work for wage” could mean any number of different activities like working in the field (active work), in a school (less active), or in an office (near sedentary). The same applies for ‘Traveling and commuting’. Therefore, predicting some time use activities using physical activity sensor signal data can be more difficult than other, more exclusive activities like ‘Fetching water’, ‘Watching TV’, or ‘Resting’
savings at NVRB. Column 3 also shows that the treatment effect on net deposits dropped to zero during the follow-up, post-commitment period. This is not surprising, since many participants withdrew their SSP savings during this period. Interestingly, in the post-survey, post-commitment period the treatment effect on net deposits (GHS 32.5) exceeds the commitment period treatment effect. This suggests that SSP had persistent effects on savings behavior at NVRB, subject to the caveat that the point estimate is not significantly different from zero.

Finally, column 4 shows that SSP generated a significant and highly persistent increase in NVRB balances: the average daily balance during the commitment period increased by GHS 244, and remained GHS 140-141 higher during the two post-commitment periods. These treatment effects are large relative to control groups means ranging from GHS 90-111 across the three focal time periods.

# **3.3 Overall Effects: Results from Survey Data**

We now turn to the survey data to assess whether impacts on NVRB balances reflect new savings or crowd out. We use a regression specification that mirrors equation 1, except _t_ now references survey round, the interaction between treatment and the post-commitment, post-survey period is dropped, and _yi_ 0 references values measured at the baseline survey.

The first two columns of Table 3 present treatment effects on NVRB savings. The first column uses the same administrative data used in the earlier section, but focuses on NVRB savings on the day of the relevant survey. In order to make the measure comparable with survey reports, negative NVRB account balances are coded as zero NVRB savings. The second column studies savings based on respondent self-reports. Both variables paint a similar picture: SSP more than doubled NVRB savings during the commitment period. The administrative data also show a marginally significant impact on NVRB savings in the post-commitment period, while the point estimate for the survey data is smaller and not significantly different from zero. Columns 3 and 4 show that SSP had no significant impact on other formal or informal savings.

Individuals may also offset commitments
regression analyses in Section 4 rely on only the data from Uganda and Mozambique. However, all the surveys still share some key common variables for descriptive analyses in Section 3.

# **3. Descriptive analyses**

# **Features of surveyed irrigation schemes**

Table 3 compares rice production and production environments among the surveyed irrigation schemes. In order to investigate the importance of irrigation water for rice cultivation, we divide farmers in Doho and Chokwe into those who have good access to irrigation water and those who do not.<sup>4</sup> Since we do not have a corresponding variable for the Sahelian studies, we show the change in water access at each scheme since the last crop season. Although some show deterioration and others show improvement, we observed in our survey that water access in all surveyed schemes in the four Sahelian countries was generally good. Some might have claimed deterioration but this seem does not to mean a severe water shortage as they used to have sufficient water and the deterioration was marginal. Hence, in descriptive analyses, we treat all the Sahelian schemes as “good access.” As another case of good water access, we show data of an irrigated rice-growing area in Asia, in our case, Laguna Province in the Philippines in 1976, 1982, and 1987 (Hayami and Kikuchi, 2000). This enables us to assess the potential of SSA‟s irrigated rice in comparison with Asia when they were at a similar stage of the Green Revolution. The similarity of the stage is determined based on the type of modern varieties cultivated by farmers at each study site (either MV1, MV2, or

> 4 In Doho, farmers facing main canals are classified into the group of good access and, otherwise, the group of not-good access. In Chokwe, those who claimed “receiving enough water in 2007” were classified into the group of good access.

6
# **Table 3. Technological parameters and innovation**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Cost of entry|_fe_= 1||
|Fixed cost of production|_f_= 0.2|Exit rate of 6%|
|Exogenous exit rate|_δ_= 0.02|Exit rate of large firms of 0.55%|
|Step size of productivity|Δ_z_= 0.2|Standard deviation of employment<br>growth of large firms|
|Scale parameter of|_hz_= 0.001|R&D ⁄ GDP = 2.7%<br><br><br><br>|
|innovation cost|||
|Curvature parameter of<br>innovation cost<br>_Note_: GDP = gross domestic product|_bz_= 13.54<br>R&D = research and|φ500≤l≤1000<br>ma<br>/φ1000≤l≤5000<br>ma<br>= 38.1%<br>development.|

_Note_ : GDP = gross domestic product; R&D = research and development.

I chose Δ _z_ so that the standard deviation of the growth rate of employment of large firms in the model would be 25 percent (annualized), following Atkeson and Bursteinn (2010), which is based on data in line with estimates from Davis et al. (2007).

I chose the exogenous exit rate _δ_ so that the model’s annual employment-weighted exit rate of large firms would be 0.55 percent, which is consistent with the exit rate for large firms in the United States, according to the Statistics of U.S Businesses database.<sup>18</sup> Note that in the model, large firms do not exit because of lack of profitability; they exit because of
and its preparedness for rapid change and innovation (as measured by R&D expenditure, the number of new patents or the digital skills of the population, and the governments’ inability to provide a long-term vision). Since these supply gaps do not necessarily constitute a binding constraint on growth and they are as much a cause as a consequence of growth, the expected growth returns from filling the gaps are uncertain. Nonetheless, they clearly point toward potential bottlenecks that must be assessed—and, if needed, addressed—as a matter of priority.<sup>38</sup> 36 The figure was constructed using the World Economic Forum’s Global Competitiveness Index (excluding those associated with macro policy and the financial sector), which classifies indices of 130 indicators into seven categories: (i) Rule of law (security, violence, quality of police and the judiciary); (ii) Market support (the quality of laws and regulations in support of market activities); (iii) Infrastructure (transportation and communications); (iv) Social capital; (v) Innovation (the country’s and the state’s preparedness for change); (vi) Health and human rights (the attention to basic human needs and rights); and (vii) Trade openness (the country’s openness to both foreign goods and foreign factors).

37 For a recent assessment of LA’s shortfalls in infrastructure, which highlights the costly implications for growth and welfare of gaps in transport infrastructure, see Perry, Guzmán and Benavides (2020).

38 To better tailor policies to country circumstances, avoid unhelpful “laundry lists” of ill connected actions, and help identify areas more amenable to reform and with larger expected impacts, policy makers can use a variety of tools 46
WPS3830

# International Benchmarking of South Africa's Infrastructure Performance

## by

## Željko Bogetić and Johannes W. Fedderke

Abstract: The paper provides a first systematic, comprehensive international benchmarking of South Africa’s infrastructure performance. The analysis focuses on four major sectors––electricity, water and sanitation, information and communication technology, and transportation. The benchmarking is performed against the relevant group of comparator countries using a new World Bank international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. Specifically, we seek to answer a number of relevant questions: How does South Africa compare on major indicators of infrastructure sector performance against the relevant country groups? What do outcome indicators tell us about the relative strengths and weaknesses of South Africa’s infrastructure vis-à-vis various income and geographical comparator groups of countries? Where are the largest deviations—positive and negative––from the benchmarks and other comparators? And how does one interpret some of these comparisons to be useful for policy purposes? The results suggest the following. Despite recent gains, access remains a major issue in electricity and, especially, in water and sanitation (particularly in rural areas), and so does performance in local telecom services. Even transport performance appears comparatively less strong than would be expected in an upper middle-income, though more in-depth analysis of comparative performance of transport may be warranted to develop a more nuanced picture, especially across different modes of transport. The main policy implication is that there remain significant needs to scale up infrastructure investments––especially in the yet unserved areas––and improve efficiency in all four major infrastructure sectors if South Africa’s infrastructure performance is to catch up with its group of upper middle-income countries.

## JEL classification: H54, H, R, O, O57

### World Bank Policy Research Working Paper 3830, February 2006

_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
proxy test used to award social support in Colombia. As such, the PEP enabled undocumented migrants to work in the formal sector and to access social services (including complete health services through the subsidized health regime).

> 1For instance, at least 74 percent of Colombian natives believes that Venezuelan forced immigration exacerbates crime and insecurity (LAPOP, 2021; OXFAM, 2023).

> 2See recent evidence from Colombia by Knight and Tribin (2020) and Mexico by Rozo et al. (2021). 3See evidence by Horn (2010); Calder ́on et al. (2011); Wirtz et al. (2014); Kelly et al. (2199).

> 4According to the latest data from Colombian migration authorities, by 2022 the number of Venezuelans there numbered approximately 2.5 million people, or about 5% of Colombia’s population.

2
**(**α)||-0.03<br>(0.03)|-0.02<br>(0.03)|
|SNE Index_ Public Space #<br>migrant HH**(**β)||-0.13** (0.06)|-0.09<br>(0.06)|
|SNE Index_ Public Space**(**α)||0.02<br>(0.04)|0.01<br>(0.04)|
|Migrant HH|0.01<br>(0.06)|0.03<br>(0.05)|0.05<br>(0.06)|
|Obs.|2,000|2,000|2,000|
|Adjusted R2|0.03|0.02|0.03|
|Controls|YES|YES|YES|
|Regional FE|YES|YES|YES|

_Notes:_ Controls include a binary variable for belonging to a household with a current migrant male spouse, respondents’ age, years of education, having children younger than 5 years, log of household’s total income, at least one among mother-in-law or father-in-law living in the household, living in an urban area, belonging to the Madheshi ethnic group, and provinces.

Table A.16. Analysis of vignettes

|Comparison scenarios||p-values associated with|t-test|
|---|---|---|---|
||Full sample|Females onlysample|Males onlysample|
|1. Change SNE only: low-low vs high-<br>low (conservative SEE & SNE vs. liberal<br>SEE & conservative SNE)|0.62|0.48|1.00|
|2. Change both SEE and SNE:<br>low-low
Against this background, this paper reports on a proof-of-concept study that aims to evaluate whether machine learning and accelerometer technology can be leveraged to accurately predict time use patterns from physical activity sensor output collected in household surveys in low-income contexts. We derive our insights from a distinctive survey of agricultural families in rural Malawi. This survey combined twice-repeated 24-hour recall-based time use data collection with concurrent sensor-driven physical activity monitoring. Our paper sits at the intersection of the research streams on time use measurement and physical activity tracking in household surveys, and the main contributions are threefold. First, we build and validate a machine learning model within our sample, by pairing the highfrequency physical activity tracking data with time use survey data for the same individuals. Specifically, we train the model for a subset of the individuals and infer the time use in the rest of the sample and compare the predicted versus observed time use outcomes. If successful and crossculturally validated, this framework offers survey practitioners the possibility of increasing accuracy in time use data while managing respondent burden by combining data from low-cost, waist-worn, inconspicuous accelerometers and time use data collection from a subsample of survey respondents. Machine learning-based imputed estimates of time allocation to specific activities can then be obtained for the part of the sample that is not part of survey data collection on time use.

Second, by combining time use and sensor data, we identify issues in recall-based time use data collection that provide motivation for improving the activity lists used in surveys and assessing the accuracy of 24-hour recall. This can potentially motivate pairing real-time physical activity tracking with real-time self-administered time use diaries, inspired by the successful experience with smartphonebased time use data collection (henceforth referred to as mobile time diaries) elsewhere in Africa (Daum et al., 2019; Kilic et al., 2024).

Third, the paper brings in evidence from a rural, low-income context regarding the feasibility and utility of physical activity tracking data collection for time use measurement – a topic that has not been the subject of previous research – based on a
|60|
|WSS|7|1|8|0|0|9|16|
|Total|40|17|45|15|17|94|134|

_Source_ : Derived from Foster and Briceño-Garmendia 2009.

_Note_ : The public sector figures are averages of actual spending for 2007–09, in the case of government data. In the case of SOEs, the average spans 2004–08 and in some cases 2004–09. Funding from external financiers is averaged over the 2002–07 period. O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development.

# How much more can be done within the existing resource envelope?

About $66 million of additional resources could be recovered each year by improving efficiency (table 14). The most vexing problem is curbing distribution losses in power and underrecovery of costs in roads and water. Reducing distributional losses could save $3 million in water and S$16 million in power annually. Every year up to $19 million is lost due to under charging for services. A better-aligned roadfuel levy could bring in an additional $11 million for roads, and raising water tariffs would capture in additional $8 million. Collection of bills for water and power services needs to be improved. Sierra Leone is losing $0.9 million per year on water and $8.3 million on power due to undercollection of bills for the services. Overstaffing in the power parastatal is quite significant. Cutting the staff levels to economically viable benchmarks could save an estimated $2.4 million on power. Improving execution of budgeted governmental funds in roads could translate into $6 million of additional financing for the roads sector and $1.4 million for the power sector annually. Looking across sectors, the power sector can benefit the most from tackling identified inefficiencies, followed by the road and water sectors.

41