rafmacalaba/data-use-mentions-extended huggingface dataset:
I POLICY RESEARCH WORKING PAPER 1891-

# Summary findings

Guzanova reviews sociological data on privatization and the development of a housing market in Russia through 1996. Using data from urban surveys largely unknown outside Russia, she also considers demand for housing and household mobility in Russia.

Since early 1997 the Russian government has increasingly focused on housing reform. Current policy calls for a reduction (in stages) of housing subsidies (for which both owners and tenants of privatized apartments are eligible), with the goal of 100 percent cost recovery by 2003. But household incomes are not expected to rise commensurately, so housing's share of the household budget is likely to grow for most Russians.

By the end of 1996 about 55 percent of Russian housing was privately owned. The rate of privatization peaked in Moscow in 1993 and has since abated considerably, essentially coming to a halt in 1996. The pattern was the same in smaller cities, but with a later starting date. Not surprisingly, high-quality apartments in city centers have much higher rates of privatization than lower-quality housing some distance from the center. Also affecting the decision to privatize are demographic characteristics of the occupants and household incomes, values, and education levels.

Privatization has produced a far-from-uniform class of owners. The two groups most likely to have privatized - their apartments pensioners and the relatively well-off - have quite different effects on the housing market. - Pensioners -the larger group are generally not inclined to move and thus exert a negative effect on - housing mobility. The well-off a much smaller group - can be expected to participate actively in the housing market.

There has been some movement toward a more efficient allocation of housing. Because of economic forces, part of the mover households moved from their original apartments to apartments that were somehow inferior. Moreoever, the housing market allows poorer households to find housing more in keeping with their ability and willingness to pay for it.

Many renters in Russia have chosen not to privatize their apartments, influenced largely by the sense of "occupation rights"
# **The Welfare Effects of Mobile Broadband Internet: Evidence from Nigeria**<sup>**1**</sup>

Kalvin Bahia<sup>2</sup> Pau Castells<sup>3</sup> Genaro Cruz<sup>4</sup> Takaaki Masaki<sup>5</sup> Xavier Pedrós<sup>6</sup> Tobias Pfutze<sup>7</sup> Carlos Rodríguez-Castelán<sup>8</sup> Hernan Winkler<sup>9</sup> .

**Keywords:** Poverty, Household consumption, Mobile broadband, Africa, Nigeria **JEL codes:** D12, F63, I31, L86, O12

1 The authors wish to thank Shubham Chaudhuri, Andrew Dabalen, Mark Dutz, Jonas Hjort, Samantha Lach, David McKenzie, Chandni Raja, Viviane Sanfelice, Emmanuel Skoufias, Daniel Valderrama and Tara Vishwanath for their comments and suggestions. The authors would also like to thank participants of the World Bank seminar organized by the Poverty and Equity and Digital Development Global Practices. The authors are also thankful to Gero Carletto and Siobhan Murray from the World Bank LSMS team for their support with data integration, as well as Aibek Baibagysh and Abul Azad from the World Bank Poverty and Equity Global Practice for their support on building the consumption aggregate for Nigeria GHS. The authors would also like to thank Mobile Network Operators in Nigeria for sharing their mobile network infrastructure data. This study received funding from the Digital Development Partnership and from UK aid. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent, or the UK government’s official policies, or those of the GSMA and GSMA Intelligence.

2 GSMA 3 GSMA 4 GSMA 5 World Bank 6 GSMA
.<sup>13</sup> Similarly, the Bloomberg high-yield corporate bond indices are the aggregate high-yield corporate bond

> 10 For 10/10/2016, we opted for future price (March strike date) to account for Columbus’s Day. 11 Their tickers are, respectively, DJUSBM, DJUSNC, DJUSCY, DJUSFN, DJUSHC, DJUSIN, DJUSEN, DJUSTC, DJUSTL, DJUSUT. These 10 indices together make up the Dow Jones U.S. Market Index (DJUS).

> 12 DJUSTS, and DJUSAR.

> 13 Their tickers are, respectively, BUSC, BUSCHC, BUSCTE, BUSCMA, BUSCFI, BUSCCO, BUSCCD, BUSCUT, BUSCIN, BUSCCS and BUSCEN.

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level that stabilizes<br>the net foreign asset position (ES approach).|

> 8 Égert (2003) provides a comprehensive survey of the literature.

> 9 Several papers have dealt with this by adopting a cross-country panel framework to incorporate a wider range of country experiences (for example, Maeso-Fernandez et al, 2004, MacDonald and Dias, 2007, Ricci et. Al, 2008, and Bénassy-Quéré et al., 2009).

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not external validity in that villages were not randomly chosen from the universe of villages. Different districts chose villages on the basis of different indicators. For example, some districts chose to include villages that had recently participated in water supply programs, whereas other districts explicitly chose to exclude such villages. The sample thus reflects the variety of ways in which government officials generally choose villages for a sanitation program so internal validity is sufficient under these circumstances. That is, the evaluation will provide estimates of the average impact expected given the way governments select villages for such programs. The impact of the different bases for the choices can be examined as part of the evaluation.

Once the IE team received the sub-village lists from the district offices for all 20 villages, they told district offices which villages were in the treatment group and which were in the control group. The district offices promised to do everything possible to make sure the treatment _dusun_ were treated and the control _dusun_ remained untreated. There was some concern by local

> 5 At least two villages were listed for every sub-district on the district lists. This allowed pairing of treatment and control communities with sub-districts as described above.

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of changes in poverty in this sample is 2002.

> 4. Without this restriction, around one-third of observations would be derived from Eastern Europe and Central Asia and another third from Latin America and the Caribbean.

> 5. Personal communications with the researchers managing the POVCAL database confirmed that CPI adjustments were made for approximately 20 countries for which the weight on food products was deemed to be excessively low. For Africa, the aforementioned World Bank study used the assumption that differences in the food budget share among demographically similar households with the same level of consumption at different points in time would indicate the CPI’s mismeasurement of the true cost of living. Strikingly, the official CPIs seem to have overestimated changes in the cost of living for the poor in 13 of the 16 countries examined.

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_5_

**Table 1. Latin American GDP Growth Rates (in** %)

|||GDP Growt|h|Slowdown<br>3|
|---|---|---|---|---|
||1997|1998|19992|1999-1997|
|Argentina|8.1|3.9|-4.0|-12.1|
|<br>**Brazil**|3.6|0.2|0.8|-2.8|
|**Chile**|7.6|3.4|-1.0|-8.6|
|**Colombia**|3.0|0.6|-4.5|-7.5|
|**Ecuador**|3.4|0.4|-7.3|-10.7|
|**Mexico**|6.8|4.9|3.6|-3.2|
|**Peru**|6.9|0.7|2.7|-4.2|
|**Venezuela**|5.9|-0.4|-7.0|-12.9|
|**Weighted Average'**|**5.4**|**1.8**|**-0.2**|**-5.7**|

**'** Weighted by 1997 GDP.

2 Forecasted for 1999 based on third quarter data 3Difference between 1999 and 1997 growth rates The idea that common external factors are fundamental explanatory variables of macroeconomic outcomes in developing countries was revisited by Calvo et. al. (1993) who elucidated the resumption of international capital flows to Latin America in the early nineties based on the role of external factors. More recently, business cycle models
|Constant|9.015***|5.830***|8.193***|6.905***|
|_Technical Inefficiency_|||||
|Manager Completed Primary Education°|0.012|0.025|0.059|0.068|
|Manager Age|†|†|†|†|
|Manager Age (squared)|†|†|†|†|
|<br>Manager Received Agricultural Extension Services°|0.045|0.074|-0.059|0.021|
|Dependency Ratio|†|†|†|†|
|Count of Agricultural Assets|0.001|0.001|0.006|0.011|
|CV of Flowering Season Rainfall (1999-2014)|-2.442|-3.691*|-3.671|-2.858|
|Constant|7.068***|7.284***|7.181***|6.990***|
|_Random Error Term (v)_|||||
|Constant|-2.047***|-1.919***|-1.910***|-1.748***|
|N|367|367|274|323|

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|---|

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Using high resolution DEM maps, we downscale and prepare the flood inundation map (i.e. flood fraction 0 to 1.0) for each AOGCM (spatial resolutions: 2.5 min x 2.5 min) (Hu _et al._ , 2015). From these outcomes, we extract the flood extent for return periods greater than 1 in 100 years, 1 in 50 years and 1 in 30 years and produce the mean flood inundation map for 2016–2035 and 2036–2055 at RCP4.5 and RCP8.5 respectively. We selected these return periods because the flood protection standard for Chinese infrastructures typically ranges between 1 in 10 years to 1 in 100 years, as required by the law.

With the above outcomes, we evaluate the extent of changing flooding probability, calculating the difference between two selected subsequent periods. Here we subtract the inundation value for each grid (spatial resolutions: 2.5 min x 2.5 min) and estimate the total flooded area between period 1 as defined by the difference between 1986–2005 and 2016–2035, and period 2 as defined by the difference between 1986–2005 and 2036–2055, for both pathways RCP4.5 and RCP8.5. This difference indicates how specified grids in China would face different levels of changing flood probabilities – increasing, decreasing or a continuation of the status quo.

# **2.2.2. Estimating impact on WWTP**

To demonstrate how Chinese wastewater treatment plants may be exposed to the changing probability of flooding, we identify those wastewater plants that face increasing, decreasing or non‐ changing flooding hazard by intersecting them with inundation cells, as estimated above. We subsequently calculate the net change, as defined by the difference between the number of assets facing increasing hazard and those that face decreasing hazard. We also calculate the number of users each wastewater plant serves, as described in Section 2.1, as well as the those that face increasing/decreasing flood hazards. Below
# 4. **Empirical Findings**

Section 4.1 present the results of a cointegration analysis over the whole sample (1965:Q1 to 1998:Q4) using quarterly real GDP data provided by the Banco de Ecuador. To test the robustness of our findings, we additionally investigate two different subsamples. Section 4.2 concentrates on the period from 1965:Q1 to 1989:Q4, section 4.3 focuses on the period from 1990:Q1 to 1998:Q4. Section 4.4 estimates a dynamic shortrun sector growth model based on the results of section 4.3. Section 4.5 disaggregates the industrial, agricultural and service sectors into their components and presents the evidence of bivariate cointegration tests between different intrasector components. Appendix 2 repeats parts of the analysis of section 4 for an alternative data set, using annual real GDP data from the World Bank Latin American and Caribbean Regional Database.

## 4.1 Evidence of Cointegration

Our sectoral growth VAR model includes a constant in the cointegration space and 4 lags of each of the variables industrial, agricultural and service sector GDP. This is sufficient to produce random errors.<sup>4</sup> '<sup>5</sup>

> 4 The deterministi **c** omponents of the VAR were defined according to the rank test based on the so-called Pantula principle (see Johansen and Juselius (1992). According to the Pantula principle three different model specifications (no linear trends in the levels of the data (Model 2), linear trends in the levels of the data (Model 3) and time-trend in the cointegration space (Model 4)) are estimated and the results are presented from the most restrictive alternative (i.e. r = 0, and Model 2) through to the least restrictive alternative (, i.e. r = n- 1, and Model 4). The critical values for Model 2, Model 3 and Model 4 correspond to Table 1*,
reference cluster. The variables and their sources are described in Appendix 2. The regressors fall into three broad categories:

**Figure 2: Correlations between Growth and the Sources of Vulnerability**

<!-- Start of picture text --> Private Sector Credit Growth (%) Fiscal Deficit (% of GDP)<br>150 20<br>100 10<br>50 0<br>0 -10<br>-50 -20<br>-10 0 10 20 30 -10 0 10 20 30<br>Loan-to-Deposit Ratio (%) REER Overvaluation<br>600 .2<br>400 0<br>200 -.2<br>0 -.4<br>-10 0 10 20 30 -10 0 10 20 30<br>External Debt (% of GDP) Short-term Debt (% of Foreign Reserves)<br>150<br>600<br>100<br>400<br>50 200<br>0 0<br>-10 0 10 20 30 -10 0 10 20 30<br>Real GDP Growth (%)<br>Cluster L Cluster V Cluster G Cluster H<br><!-- End of picture text --> Sources: IMF’s World Economic Outlook database and authors’ calculations.

Notes: The lower growth and lower vulnerability is identified by L, the lower growth and higher vulnerability is identified by V, the higher growth and lower vulnerability is identified by G, and the higher growth and higher vulnerability is identified by H. The absence of higher growth, higher vulnerability data in 2000 and 2008 reflects the lack of countries in such cluster.

- <u>Initial conditions. These comprise measures that may be susceptible to policies and institutional</u> changes over a relatively long period. This category includes measures that reflect a country’s economic development and its economic structure, as well as the degree to which
IOp.<sup>20</sup> IOp and output per capita also has a stronger negative relationship than total inequality and output-per-capita, consistent with existing evidence that IOp is relatively more important for growth than total inequality (Marrero et al. 2013, Bradbury et al. 2016, Carranza 2020).

Finally, in Figure A3, we show the relationship between comparable IOp and intergenerational mobility (IGM) calculated for our countries of analysis by van der Weide et al. (2024). We measure intergenerational mobility as 1 minus the correlation coefficient between respondents’ and parents’ years of schooling. Globally there is a negative relationship between IGM and IOp, typically seen as driven by the fact that IOp decreases as parental socioeconomic characteristics become a less relevant predictor of income inequality (Brunori et al., 2013). This pattern persists as our estimates from SSA are included with global data.<sup>21</sup> However, the negative global correlation between IOp and IGM is relatively moderate (R<sup>2</sup> =.45), highlighting the value of IOp as a standalone measure of inherited inequality that carries additional information.

# Section 6: Conclusion

In this paper, we estimated IOp across 18 countries in SSA (the largest sample among existing studies on IOp in Africa) using the most recently available surveys, a large set of circumstances, and a machine-learning method that trades off upward and downward bias, reduces the need for arbitrary specification choices, and reveals the unique ways in which birth characteristics explain inequality across countries. The results show that more than half of inequality in SSA is explained by circumstances at birth. On average, IOp explains 54 percent of overall inequality in consumption, which is 15 percent higher than previous estimates in the region.

The outsized role of conditions at birth in determining lifelong income is concerning from a moral perspective, but also because existing evidence indicates that IOp is a more important determinant of growth than total inequality (Marrero et al.
2 The second page adds results for every 25 years from 2000 to 2150 (as well as rates for the 1985-2000 period). This page provides fewer indices, including only the population by age and sex, the birth, death, net migration, and growth rates, total fertility, life expectancy, and infant mortality.

Most of these numbers are standard demographic indicators, but the infant mortality rate requires some qualification. More properly labeled lqo, it is an index extracte from a Coale-Demeny model life table chosen to provide an appropriate life ..xpectancy. It need not, and often will not, correspond to an observed infanit mortality rate.

# Methods and Data Sources

The key elements in these projections are base-year (mid-1985) total population estimates and age-sex structures, base-period (1985-90) mortality, fertility, and migration rates, and assumed trends in the rates. These elements and additional assumptions that are made will all be described.

The basic methodology is identical to that used last year (see Zachariah and Vu 1988). Those familiar with the previous publication may therefore wish to skip to the summary of results. Data and sources have been updated; an Appendix indicates the sources for all base-year or baseperiod estimates.

# Total RoDulation and a&e-sex comRositiOn

The estimates of total population in mid-1985 come from diverse sources. Where possible, estimates are Bank projections from reasonably complete censuses or official figures from governments with comprehensive population registration. Other- official figures are also used, either from the U.N. PoRulation and Vital Statistics Re_ort or from government publications. When these figures do not pertain to 1985, they are projected forward. Other sources of estimates include the U.N. 1988 revision of world population prospects (forthcoming), the U.N. 1984 assessment (published in 1986), Eurostat (1987), the U.S. Bureau of the Census (1985), and Celade (1987). The specific source for each country is listed in the Appendix.

The 1985 population
etc.; and 2) conditions on the service supplier – in other words, measures affecting the 'production' of services, e.g. local content and performance requirements. Measures affecting government procurement of services are also captured in category B.

Category C covers measures related to competition. While this category and the next could arguably be part of conditions on operations, we treat them as separate to ensure compatibility with the OECD database. This category encompasses measures that grant rights or prerogatives to state-owned suppliers, as well as measures aimed at countering anticompetitive behavior or restrictive business practices.

Category D deals specifically with the regulatory framework and administrative procedures, focusing on non-discriminatory regulations – other than those already covered elsewhere in the classification – that may still have an impact on trade in services. Conscious of the efforts of governments to establish disciplines in new areas, we strove to include measures pertaining to the regulatory framework (e.g. licensing requirements and procedures, review mechanisms, regulatory transparency) as well as measures affecting cross-border data flows. Issues such as regulatory transparency, the nature of the regulatory authority, the application of international standards, or administrative procedures and requirements are captured by this category.

In addition, the classification identifies within each sub-category, whether measures discriminate against foreign provision or not. The latter is done by allowing for the inclusion of measures which are discriminatory by nature (e.g. foreign equity limitations), and by distinguishing, whenever relevant and to the maximum extent possible, between measures that apply to all services and service suppliers as opposed to those applying to foreign ones only.

The meta-classification of measures described above is intended to facilitate convergence towards a single classification and quantification methodology. Therefore, the classification remains, at the measure level, fully compatible with the classification utilized in the OECD Services Trade Restrictiveness Regulatory Database and the previous classification of measures developed by the World Bank 10 years ago.

# _Data collection methodology_

For the purposes of data collection, economies and information sources were divided into two groups
12 For net suppliers of labour or net demanders with significant nonfarm income, the welfare effects of a change in foodgrain price depend crucially on both the expenditure share devoted to foodgrains and the income shares from agricultural labour and own-production of foodgrains. These can be estimated from standard income-expenditure surveys. The present discussion will be mainly based on results of the 1978-79 household expenditure survey for Bangladesh as reported in BBS (1984), although supplementary data from other sources will also be considered.

It is clear that, on average, non-foodgrain expenditures (qy) dwarf non-farm incomes (Z) in rural areas for all except the very poor households; see, for example, BBS (1984, Tables 15.18 and 15.28). Thus it can be safely assumed that V*>l for net demanders of labour, and so they will unambiguously gain from an increase in the price of foodgrains.

The welfare effect on net suppliers of labour is more contentious. From BBS (1984, Table 15.28) one finds that the mean share of wages in income for the poorest 55 per cent of rural households in 1978-79 was 0.48, although this varies a good deal according to income, falling sharply from 0.62 for the poorest households to 0.38 for the least poor households (Table 1). The same source gives estimates of imputed incomes from non-market activities for this group of households; this has a mean of 0.28 and, as can be seen from Table 1, varies little by income (0.24 to 0.30). "Business income" varies a good deal more, tending to increase with income (Table 1).
to the sector, It appears that credit to the public sector does decline in importance after privatization, and that this portfolio shift does coincide with improved portfolio quality.

Individual bank data confirms this observation. Table 10 shows public sector credit data before and after privatization for the eight banks that privatized prior to April, 1996. With the exception of Entre Rios, the privatized banks have substantially lower nominal credits to the public sector. Interestingly, the banks that now have the higlhest shares of bad credits are those that had the highest shares of public credit prior to privatization -- the two worst performers, La Rioja and Corrientes, loaned over 90% of their pre-privatization credit to the public sector. It is possible that these banks lacked both experience lending to the private sector and an established private clientele. In summary, when the pre-privatization share of credit to the public sector was low, and where there was a substantial reduction in nominal public credit, portfolio quality (as measured by the share of bad credits) has improved substantially. In fact, the average share of bad credit for the five banks satisfying these criteria is a little over 5%, slightly _lower_ than for the private bank sample from 1994-96.35

- **3S** Admittedly, the low shares of bad credit for some banks in Table 10 may merely reflect that all bad credits passed to the residual entity. Their "steady-state" bad credit shares may turn out to be a bit higher.

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