rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3 CPHS to benchmark Comparing surveys**

Our starting point is a CPHS dataset containing one observation per household per year, where consumption is reported with a one-month recall and individual level sampling weights reflect the observed population distribution. Nominal consumption expenditures in both the CPHS and NSS surveys are deflated to 2011-12-rupee prices using monthly CPI-IW and CPI-AL price indices for urban and rural observations, respectively. We also adjust for spatial price differences using 2011 PPP exchange rates from the International Comparison Program following Atamanov, et al. (2020).

## **3.1 Non-expenditure variables**

_Demographic characteristics:_ According to Somanchi (2021), the share of children under the age of 10 in CPHS-2019 is 8.9 percentage points lower than the official sample registration survey (SRS) of 2018. This under-coverage is balanced by shares of people aged 40 to 65 years being 11.9 percentage points higher in CPHS-2019 than SRS 2018. CPHS also reports a higher share of households with 2 to 5 members but undercounts households with either a single member or those with more than 6 members. Finally, the CPHS is seen to over-represent Hindu households compared to the benchmark surveys such as NFHS-4.

Figure 2 compares trends in key demographic indicators using the NSS-2011 consumption expenditure survey, the NSS-2014 survey on services and durable goods consumption and the PLFS surveys of 2017 through 2019 as the nationally representative benchmark surveys. The figure shows both the magnitude of the biases observed in the CPHS and the extent to which these biases are corrected by means of reweighting the CPHS. The distribution of household size and its trend estimated using the CPHS now closely match the estimates observed in the nationally representative NSS-surveys. The over-representation of Hindu households is also accounted for. The population shares for other religions similarly match with those observed in the NSS surveys. Biases observed in the composition of scheduled caste, scheduled tribes (and other classes), share of female headed units and households with extended family members living in the same house are also largely resolved through
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# Figure A4. The Progression of Severity: Low Cases

<!-- Start of picture text --> Afghanistan Australia Bangladesh Egypt, Arab Rep.<br>2.5<br>11<br>2.0 12<br>13<br>1.5 20<br>1.0<br>0.5<br>0.0<br>Indonesia India Iraq Japan<br>2.5<br>2.0<br>16<br>1.5 18 16<br>1.0 20<br>0.5<br>0.0<br>Korea, Rep. Morocco Malaysia Nigeria<br>2.5<br>9<br>2.0 11<br>1.5 20<br>20<br>1.0<br>0.5<br>0.0<br>Pakistan Sudan Ukraine South Africa<br>2.5<br>2.0 13 11 12<br>1.5 11<br>1.0<br>0.5<br>0.0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization.

Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

33
simulating the estimated change in the uptake among users and the corresponding expected change in consumption, which represents a novelty of the approach.<sup>23</sup> For model selection, the analysis implements a backward stepwise probit regression on per capita consumption (in logarithmic scale) and observable household characteristics. Based on the results of this model, it applies the proportional income change to estimate the marginal effects of competition, resulting in the predicted probabilities of usage.<sup>24</sup> For the analysis, a variable selection filter with a 5 percent threshold of statistical significance was used. Covariates in the probit model include characteristics of household heads (employment status, literacy, sex, and age) and household characteristics (access to electricity, material of dwelling’s walls and floors, and household size). After estimating the predicted probabilities of demand, the analysis imputes the expected expenditures for those households that were previously priced out of the market.<sup>25</sup> Specifically, estimates are calculated by running a quantile regression model with a random imputation of residuals.<sup>26</sup> The results show that a 25.3 percent drop in the price of mobile services would increase the uptake of mobile services by 5.2 percentage points with respect to the baseline of 42.0 percent. The increase in demand is 1.6 percentage points among households in the lowest consumption decile, while it corresponds to 6.1 percentage points among households in the top decile. The rise in the share of users because of the greater competition is then associated with an additional reduction in poverty of 0.09 percentage point with respect to the baseline 24.2 poverty rate (table 5). New users do not have a significant effect on the Gini index (which is thus not reported in table 5).

**Table 5. Poverty Impacts, Loss of Ethio Telecom monopoly, New Users**

|_Scenario_|_Poverty indicators_<br>
augur well for credit quality. Turkish banks may be subject to large credit losses if economic activity slows down, for example, due to business cycle reasons.

**Table** 27

**Turkey: Growth of Bank Credit to the Private Sector**

||**Relative to the**|**Growth of GNP**||
|---|---|---|---|
|Year|Real Credit Growth|Real GNP Growth|Difference|
|1992|14.3%|6.4%|7.9%|
|1993|15.3%|8.1%|7.2%|
|1994|-32.0%|-6.1%|-25.9%|
|1995|44.0%|8.0%|36.0%|
|1996|27.7%|7.1%|20.6%|
|1997|22.5%|8.0%|14.5%|

Source: own calculation based on IFS.

21 The level of non-performing loans was reported to have increased sharply during 1998, as a consequence of adverse economic conditions.
the environmental policies drive the differences in the emissions. Second, it uses a new measurement of informal economy – the share of informal workers in total employment which is based on data and not on estimates which are sensitive to approaches and assumptions that could potentially bias the results. Finally, the paper studies the impact of informal employment on emissions using country- and sector-level data and estimates the aforementioned relationship on the sector level. This is important given the considerable differences between sectors in terms of their productivity, capital intensity, and competitiveness, which might affect the aggregate country-level results.

The estimation results using country and sector fixed effects show that there is no significant impact from informality on either CO2 or non-CO2 emissions. This implies that emissions per value added in the informal sector are greater than in the formal sector as shown in the theoretical model.<sup>8</sup> Conducting these regressions separately for nine sectors - agriculture, manufacturing, trade, mining, construction, finance,

> 8 The model uses a decomposition approach to derive the relationship between the estimated coefficients and informal emissions per value added.

4
in demand (ii) disruption in production and supply chains (iii) unavailability of liquidity and financial services (iv) uncertainty of the extent and time horizon of the shock, (v) lock-down restrictions (Apedo-Amah et al., 2020).

> 3Moreover, the World Development Indicators (WDI) reveal that female labor force participation rate in South Asia remains low at 22% in 2020, compared to LAC (41%), EAP (43%), ECA (45%), and SSA (46%), and are likely to be concentrated in vulnerable sectors (Mukhtarova, 2020).

2
proposals which could be funded at opportune times, in provinces deemed to be electorally important. Polls suggest that FONCODES projects are closely identified with the presidency and with President Fujimori himself (Moncada, p. 62).

But FONCODES also appears to have had an important redistributive function. My results show that FONCODES funds flowed disproportionately towards poor provinces. Household survey data indicate that the infrastructure constructed by FONCODES reaches a larger fraction of households, especially poor households, than other programs in the Ministry of the Presidency (Paxson and Schady, World Bank 1999). Such success reaching the poor can in part; be explained by the fact that FONCODES has been free of many of the restrictions, paperwork and inefficiencies that have given the public sector in Latin America a bad name.

Was FONCODES, then, an effective poverty alleviation program or a brazen attempt to buy votes?

**28** Paxson and Schady present nonparanetric regressions and "concentration curves" for FONCODES expenditures on education projects, by year. The curves show that the cumulative fraction of resources flowing to poor districts was unambiguously higher in 1994 and 1995 than in 1992 and 1993.

25
||NA|0.6|2.45|18.0%|23.0%|
|<br>Philippines|33.4%|Universal banks:<br>US$ 115 million<br>(P 4.5 billion)<sup>21</sup>|NA|0.2|2.88|17.5%|36.8%|
|Thailand|46.8%|US $ 270 million<br>(Baht I billion)|NA|0.2|2.43|22.2%|10.6%|

Source: World Bank data, unless otherwise indicated.

_'9_ Source: Qian, Ying (1999). Scale: 0 to I with I most open.

20 Scale: I most closed, 5 most open. Index is average of banking and securities indices of degree of openness, in practice. 21 Under discussion.
# **Figure 1C: Moderating effect of congestion by foreign ownership status**

<!-- Start of picture text -->
-.2 -.15 -.1 -.05 0 .05 .1 .15<br>Congestion (Mean=0)<br>Domestically owned Foreign-owned<br>.8<br>.6<br>.4<br>.2<br>0<br>-.2<br>-.4<br>Effect of Urban Density (ln)<br>-.6<br>-.8<br><!-- End of picture text -->

Note: The data for these graphs come from the results in Table 4, Panel C (Congestion) and the x-axis is based on the full-range of the variable. Please note that we look here at the differences between slopes and not the difference in point estimates. The interaction term (presented in Table 4) tells us whether or not the marginal effect of urban density depends on the value of lack of mobility. Since this coefficient is significant, we would conclude that the interaction effect of urban density and lack of mobility depends on the sector/export/ownership status. The p-values of the point estimates answer a completely different question, namely whether the groups at a specific point in the distribution differ from each other.

# **5. Concluding remarks**

We use firm-level, geocoded data on 51,000 establishments in 649 cities in 98 developing countries, from the World Bank Enterprise Surveys and a new global database of city-level mobility and congestion indexes from Akbar et al. (2021) to unpack the relationship between urban density and firm productivity. We estimate the “pure” firm productivity gains of urban density in the developing world, net of negative externalities associated with congestion, crime, and pollution. Our results show that while on average the agglomeration economies in the developing countries are comparable in size to
The information on the access to ICDS programs is available only at the village level. There is no information on which households and children within the village have actually benefited from the program.

# **_Main constructed variables_**

To assess household economic status in the absence of household income or expenditure data we construct, following the methodology of Filmer and Pritchett (2001), a linear index from a set of asset indicators using principal components analysis to derive the weights for each asset indicator. Our economic status index is the fist principal component of a number of household assets such as clock, radio, TV, VCR, refrigerator, ownership of bicycles, motorbikes, cars, as well as the type of utilities used in the household. The first principal component is an unobserved vector that explains the largest amount of variability in the observed data. The household assets based first principal component derived from NFHS-1 data accounts for 29.6 percent, and from NFHS-2 for 28.3 percent of the total variance of the relevant variables<sup>17</sup> . Availability of electricity, flush toilet, TV, and ceiling fan are the most influential variables in the estimation of the index. This finding is consistent across both surveys. The distribution of other factors in relationship to the economic status index also makes economic sense. For example, households with a higher wealth index are more likely to live in pucca (brick) houses, and have such amenities as refrigerator, motorbike and radio. They are less likely to use kerosene for lightning and wood for cooking, as well as utilize unsafe drinking water.

Our main indicators of children’s nutritional status are two indices that are commonly used to assess this from anthropometrical data. These indices are expressed in standard deviation units (z-scores) from the median for the international reference population (Dibley et al 1987a, 1987b).<sup>18</sup> Height-for-age z-score (HAZ) and weight-by-age z-score (WAZ) are defined as _(mi-mr)/
and subsequently keep these lists updated. Collecting phone numbers from more than one adult household member increases the chances of successfully re-contacting the household for any future survey. Updating and maintenance of the list of phone numbers may be done through brief follow-up calls or surveys though one needs to be mindful of inducing increased attrition via too many contacts. When possible, working with local officials or community leaders to update and maintain lists may be beneficial. This approach proved successful in increasing coverage ex-post in a recent World Bank Africa Gender Innovation Lab survey of rural agricultural households in Western Uganda (Sharma et al., 2021). An important additional consideration is to ensure that respondents can give informed consent to their phone numbers remaining on file for follow-up interviews (Glazerman et al., 2020).

In a situation where no recent previous phone contact information is available, survey designers can create a new sampling frame of phone numbers through in-person visits, listing households, their phone numbers, and some basic demographic information in target enumeration areas much like listing is done in traditional in-person surveys. The newly created lists can then be used for follow-up phone surveys.

Coverage at the household level may be increased by collecting contact information from a reference person outside the household, such as a friend, relative, or neighbor, particularly in situations when none of the household members own a phone. In the HFPS that drew upon the LSMS-ISA surveys, some of which used this approach, the availability of reference person contact information helped not only to retain households that do not have phone numbers of their own but also facilitated contact with households that could not be reached on their own phone(s) but were reached through a reference person’s. The share of HFPS respondents who were ultimately reached through reference person contact information ranged from 7 percent in Burkina Faso to 20 percent in Malawi. In all cases, the share of respondents reached through reference contacts was higher for rural and agricultural households with a substantial 22 and 23 percent of agricultural households reached through reference
, but the overall findings are largely consistent. The following conclusions emerged: (i) PMT selects households with lower per-capita consumption than CBT, but no method makes a large difference; (ii) targeting transparency and appropriation is low regardless of the method employed; (iii) hybrid methods do not work well in comparison to each individual method; (iv) little is known about the legitimacy of different methods and about the role of targeting in the overall program effectiveness.

Our study contributes to the scarce literature that both compares different methods and makes comparisons across countries in a harmonized manner, by directly analyzing household data collected from various programs implemented in the Sahel.

> 17 See for instance Besley & Kanbur (1991), Sen (1992) or Cornia & Stewart (1993) for earlier studies.

> 18 By focusing on the Sahel region and adjusting selection threshold rates when possible, we control for differences in context and coverage rates – and do not find large within-targeting method variations.

11
Policy Research Working Paper 5904

# **Abstract**

This is the first paper to build a comprehensive empirical picture of power pricing practices across Sub-Saharan Africa, based on a new database of tariff structures in 27 countries for the years 2004–2008.

Using a variety of quantitative indicators, the paper evaluates the performance of electricity tariffs against four key policy objectives: recovery of historic power production costs, efficient signaling of future power production costs, affordability to low income households, and distributional equity.

As regards cost recovery, 80 percent of the countries in the sample fully recover operating costs, while only around 30 percent of the countries are practicing full recovery of capital costs. However, due to the fact that future power development may be based on a shift toward more economic technologies than those available in the past, existing tariffs look as though they would be consistent with Long Run Marginal Costs in nearly 40 percent of countries and hence provide efficient pricing signals.

As regards affordability, today’s average effective tariffs are affordable for 90 percent of today’s customers. However, they would only be affordable for 25 percent of households that remain unconnected to the grid. Tariffs consistent with full recovery of economic costs would be affordable for 70 percent of the population. As regards equity, the highly regressive patterns of access to power services, ensure that subsidies delivered through electricity tariffs are without exception also highly regressive in distributional incidence.

The conclusion is that achieving all four of these policy objectives simultaneously is almost impossible in the context of the high-cost low-income environment that characterizes much of SSA today. Hence most countries find themselves caught between cost recovery and affordability.

This paper is a product of the Sustainable Development Unit, the 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://econ.worldbank.org. The authors may be contacted
completely outdated, in the past years, Kenya has made important improvements both in infrastructure and health service delivery, which might not be captured by the present analysis. Nevertheless, for the purpose of the analysis, we use the most up-to-date data available.

Limitations of geospatial data The main limitation of geospatial data is that the information is not as granular as for the SDI data set. For example, the Master Facility List, although the most updated list of health centers in Kenya, contains very limited information about each facility (i.e., level of the facility and ownership). Additionally, the calculations for other geospatial data are often predictions for small areas based on multiple data sources, and in some cases the calculations based on sources like MAP might be challenging because of the geospatial coverage range capacity. This makes it less precise for the analysis as compared to SDI data collected directly from health facilities and more susceptible to capture other geographical aspects such as urban/rural status. Also, the information estimated with geospatial data is less specific than SDI data; for instance, it is only possible to study access to electricity (a dummy variable) instead of the quality of the connection at this stage.

> 17 Other dimensions of quality of health service delivery were also considered but not included in this paper. For instance, one attractive aspect of the 2018 SDI is that it included an exit interview for users, capturing the supply side of the services and the perceptions of the demand. Nevertheless, the sample of users is small (up to five patients per facility, with many facilities having only one patient interviewed), questions were only asked about family planning services, and there is no variation in the inpatients' perceptions of quality.

11
<!-- Start of picture text --> Figure 12. Net cash position of households under alternative reforms<br>100%<br>2014 Baseline<br>80%<br>Includes Family 500+<br>60%<br>Family 500+ and Tax-free<br>amount PLN 5000<br>40%<br>Family 500+ and Tax-free<br>amount PLN 8000<br>20%<br>0%<br>-20%<br>-40%<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Market income deciles<br>Source: World Bank estimates based on HBS 2014.<br>Percent of original market income<br><!-- End of picture text -->

# **VII. Conclusions**

The analysis presented in this paper is complementary to existing work analyzing the distributional impact of taxes and benefits. As previous work has shown (Paulus, 2015; Myck and Najsztub, forthcoming), the combined effect of taxes and social spending helps to substantially reduce inequality in Poland, in line with other EU countries, with most of the reduction in inequality largely being achieved by pensions. This paper also shows the important role of in-kind transfers in the form of education and health. Moreover, we find that the combination of taxes and social spending also leads to an important decline in extreme poverty. However, in cash terms, households beginning in the second decile were net payers to the treasury in 2014, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population.

In terms of the specific fiscal interventions, we find that most direct taxes are progressive and equalizing with the exception of agricultural taxes and farmer contributions, which depend on land size rather than on the income generation capacity of households. In addition, although direct taxes and contributions are progressive, they are also poverty increasing as they pose