rafmacalaba/datause-encoder-data huggingface dataset:
condominium buildings and grounds is still in many cases undertaken by public companies at below-cost payments.

**Table 4. Ownership of Apartments and Buildings in Studied Countries as % of Housing**

|||**Stock**<br>|||||
|---|---|---|---|---|---|---|
|**% of housing stock***|**Armenia**|**Lithuania**|**Poland**|**Romania**|**Russia**|**Serbia**|
|Apartment units|45.2|62.0|71.2|42.8|71.4|29.9|
|in public ownership|5.8|3.8|13.8|1.3|31.4|2.3|
|inprivate ownership|39.4|58.2|57.4|41.5|40.0|27.6|
|in buildings with<br>condominiums|5.9|8.7|5.8|20.7|5.0|n/a|
|_in buildings with whole_<br>_ownership structure*_<br>_in other buildings_|<5%<br>>33%|<5%<br>>50%|~60-65<br><5%|<5%<br>>20%|<15%<br>>50%|<5%<br>>25%|
|in buildings with scattered<br>public-private ownership|>5%|>10%|almost<br>nil|14.5|36.0|>5%|

_* Estimated_ _Sources_ : Apartment sector size: National Statistical Offices. Ownership status: LSMS, HBS, and Lithuania Household Survey. Condominium status: Armenia, UN-ECE; Morkunaite, R., et al. (2003); Poland, National Statistical Office; Russia, Ellis (2003). Buildings in other whole ownership structure and other buildings: Authors’ assessment. Buildings with scattered public-private apartment ownership: Romania, UN-ECE; Russia, Ellis (2003); the authors’ assessment.

*Buildings owned by companies, individual landlords, cooperatives, or government.

- Because of the incomplete privatizations, mixed public-private apartment ownership currently characterizes almost the entire Russian M-F housing stock. Condominiums and cooperatives have been created in only an estimated 7 percent of the stock<sup>32</sup> ; the ownership of common areas and land in the remainder is ambiguous. Because maintenance has rarely been privatized, local governments spend on average 70 percent of their budgets on housing and communal services, making the owner-occupied sector heavily subsidized and essentially a form of public rental. The situation is exacerbated by large number of entitlements ( _l’goti_ ) granted to numerous population categories in the form of exemptions from and abatements of housing and utility payments.<sup>33</sup> Because cost recovery is still largely insufficient at ratios of about 60 percent, persistent deferred maintenance and insufficient energy conservation measures continue to trouble the sector.<sup>34</sup>

- In Serbia, mixed public-private apartment unit ownership is still common because many units remained un-privatized owing to quality or tenant idiosyncrasies. There is no reliable information about the share of condominiums in the apartment stock, but it appears to be rather very low. It is not clear who owns the common areas and land in the

> 32  See UN-ECE (2004b) and Skyner (2004).

> 33    The government has recently boldly eliminated or “monetized” many entitlements, but left those pertaining to housing and communal services to be tackled later.

> 34  See Ellis (2003).

21
-54-

------------------------------------------------------------------------

__---__--------

|Quantities<br>(Value<br>**Added.**<br>or|**Formr**<br>Manufacturi<br>at Mexi|TABLE**3.16**<br>**National**<br> <br>ng.<br>Mexi<br>can "pric|**Accounts**<br>co/USA.<br>es"|**Concept)**<br>**b**<br>1975<br>at|**y NaJor**<br>US "pri|Branch<br>ces"|
|---|---|---|---|---|---|---|
||Mexico<br>1975<br>(1975 Ps.|USA<br>1975<br>million)|Mexico/<br>USA<br> (%)|<br>Mexico<br>1975<br>(1975 USS|<br>USA<br> <br>1975<br>million)|Mexico/<br>USA<br> (X)|
|Food Products|20,446|<br>245,29|6 8.34|<br>3.866|<br>25,42|1 15.21|
|Beverages|8,170:|<br>91,04|6 8.97|<br>462|<br>5,155|8.97|
|Tobacco Products|1,177|<br>23,07|3 5.10|<br>154|<br>2,469|6.24|
|Textiles and Wearing Apparel|15,334|<br>379,89|0 4.04|<br>879|<br>20,32<br>|5 4.32<br>|
|Footwear and Leather Products|2.472|<br>30,04|4 8.23|<br>218|<br>2,339|9.34|
|Wood and Paper Products|13.121|1,042,76|2 1.26|<br>662|<br>45,78|81.44|
|Chemical Products|26 <sup>,</sup>2 26 <sup>ab</sup>|<sup>363,469</sup>|<br>7.22|2,600|32,627|<br>7.97|
|Rubber and Plastic Productss|6,264|301,708|<br>2.08|214|9,231|<br>2.32|
|Stone, Clay and Glass Products|8,857|122,755|<br>7.21|715|10,817|<br>6.61|
|Metal products|23,949|741,602|<br>3.23|2,129|52,617|<br>4.05|
|Electrical Machinery|9,557|243,997|<br>3.92|839|27,369|<br>3.06|
|Machinery andTransport|||||||
|Equipment|19,423|1,086,945|1.79|1,434|79,087|<br>1.81|
|Other|2,494|225,228|1.11|228|15,099|<br>1.51|
|Total|157,488|4,897,816|<br>3.22|14,401|<br>328,343|4.39|

a) indirect taxes and subsidies are deducted (see table 2.3).

b) includes 3,831.7 million pesos (excl.indirect taxes and subsidies ) for petroleum refining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

- Note: The breakdown between food products and beverages for the US on a national accounts basis was assumed to be proportionately the same as on a US Census basis (1975 figures derived from Annual Survey of Manufactures).

- Source: Mexican value added in national currencies from Resumen General (see table 2.3 which does not exclude bank costs). US value added in national currencies from National Income and Products Accounts of the United States: 1929-76 Statistical Tables (1981c) after adjustment for inventories indirect taxes and subsidies and net intrest (see table 2.6). PPPs from table 3.14.
<!-- Start of picture text --> a. Shares of high- and low-potential b. Employment effect of a single additional job<br>self-employed workers in total employment created by high-potential self-employed workers<br>70 9<br>60 8<br>& ey 7 A<br>g 50 f 6 e e<br>= 40 Bs Er °<br>g= 30 =g 4 ° ee ee<br>x Ss<br>=a 20 sF= 22<br>10 1<br>oO oO<br>500 3,000 15,000 500 3,000 15,000<br>per capita GDP, US$ 2005 PPP per capita GDP, USS 2005 PPP.<br>® low-potential self-employed workers @ employers<br>® high-potential self-employed workers<br>Sources: Gindling and Newhouse 2012 for the World Development Report 2013; World Development Report 2013 team estimates based on data from 36 countries.<br>Note: GDP = gross domestic product. PPP = purchasing power parity. In panel b, each dot represents a country.<br><!-- End of picture text -->
Policy Research Working Paper 7994

# **Abstract**

It is theoretically ambiguous whether growth of cities matters more to the rural poor than growth of towns. This paper empirically examines whether growth of India’s secondary towns or big cities mattered more to recent rural poverty reduction, noting that data deficiencies have made this a difficult question to answer previously. Satellite observations of night lights are used to measure urban growth on the extensive and intensive margins in the context of a spatial Durbin fixed-effects model of poverty measures for rural India, calibrated to a panel of 59 regions observed four times over 1993–2012. The expansion of lit area had greater effect on the rural poverty measures than did intensive margin growth in the brightness of light from urban areas. For India’s current stage of development, growth of secondary towns may do more to reduce rural poverty than big city growth, although the theoretical model suggests that cities may eventually take over from towns as the drivers of rural poverty reduction.

This paper is a product of the Poverty and Equity Global Practice Group. 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 at rmurgai@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
While here we define Water Plant partly based on the changes in plant water coverage in a village, we do consider alternative definitions. The results are quite robust. In Table A1, we construct a variety of treatment variables by employing different cutoffs of coverage changes or by relying on cutoffs of the coverage directly. The estimates of the treatment effects remain similar across these definitions. For example, the estimate of the gain in the grades of education completed is in a narrow band from one to 1.2 years across all of these definitions.

Another legitimate concern is about our sample composition. We have used the observation of the last year between age 18 and 25 for each individual for his/her final schooling attainment. Age 18 is supposed to be the year of high school graduation, and the vast majority of rural youth would have finished their schooling. However, sometimes rural children delay their schooling, which would make graduation from high school slightly later than age 18. We thus repeat our baseline regressions, but the final sample would consist of the observation of the last year between the age 19 (or 20, 21) to 25 for each individual. The new restrictions reduce the sample somewhat (to 4572, 4402, 4207), and the estimate of the effect of Water Plant on grades of education completed become 1.09, 1.12, and 1.15, respectively, all statistically significant at the 1 percent level.<sup>24</sup>

# **Cost Effectiveness**

While the effect of the rural drinking water program is large, it remains unclear whether the program is cost effective. Yet some notion of costs and benefits of such program is important. After all, China’s fiscal expenditures have been increasing dramatically over time: the total government expenditure was 706.1 billion Chinese yuan in 1989, and it becomes 14 times larger in 2011 (10,924.8 billion) (China Statistical Yearbook, 2012).<sup>25</sup> With such a grand scale of government spending, it is important to understand whether the fiscal resources are allocated cost-effectively, and this requires knowledge about the returns to various social programs funded by fiscal expenditures. To evaluate the current program and to help guide future fiscal allocation, we thus conduct a back-of-the-envelope analysis of the costs effectiveness of this program.

> 24 The table is not reported and is available upon request. 25 All numbers here are in 2011 value.

15
McKenzie 2011). Another critique of the approach is that by aggregating average trends for a given group (or cohort) it assumes away key intra-group mobility, which may be equally or more important than aggregate mobility. A recent approach developed by Lanjouw et al. (2011) explores movements in and out of poverty by imposing fewer restrictions than earlier literature on pseudo-panels. The method produces lower and upper bound estimates of mobility which are expected to sandwich _true_ mobility estimates obtained from actual panel datasets.

This paper seeks to validate this “synthetic panel” approach by estimating intragenerational mobility by means of repeated cross-sectional surveys. We focus on Chile, Nicaragua, and Peru; three countries for which we have panel datasets that follow households for more than two rounds. This allows us to compare true estimates of mobility using the three panel datasets against mobility estimates from applying the Lanjouw et al. (2011) synthetic panel approach in which individual rounds of the panels are treated as though they were repeated cross-sectional surveys.<sup>1</sup> In doing so, the paper makes four contributions to the existing literature. First, the study performs a range of sensitivity analyses and robustness tests to help validate the methodology proposed by Lanjouw et al. (2011) and probe its underlying assumptions.

Second, we improve on Lanjouw et al.’s (2011) inability to use retrospective information to improve the estimates. In their application to Vietnam and Indonesian data, Lanjouw et al (2011) force cross-sectional information to be time-invariant and retrospective by making use of the two-year panel structure of the dataset – i.e., they insert the value of variables from the first round into the second round of the panel. This provides an “ideal” but not entirely realistic context within which to apply the method. By contrast, our surveys include the information

> 1 The use of “true” mobility in this paper relates to how close the approach comes to replicate panel data results. It remains that the typical panel data set  may itself suffer  from measurement error and high attrition rates, resulting in error-prone estimates of mobility.

3
|Health Sector Reform Project|Sheila Dutta|
|---|---|
|Health Sector Reform Project|Sheila Dutta|
|Health Sector Reform Project|Sheila Dutta|
|Health Sector Reform Project|Sheila Dutta|
|Health Sector Reform Project|Sheila Dutta|
|Health Sector Reform Project|Sheila Dutta|
|Health Sector Reform Project|Sheila Dutta|
|Health Sector Reform Project|Sheila Dutta|
|Debt & Risk Management IDF Grant|Kamer Karakurum-Ozdemir|
|Fiscal Decentralization IDF Grant|Alia M. El Moubayed|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|Coastal and BiodiversityManagement Project|Liba Chaja Strengerowski|
|PRSC 3|Gregor Binkert|
|PRSC 3|Gregor Binkert|
|SL-Fourth Economic Rehabilitation and RecoveryGrant|Douglas M. Addison|
|SL-Fourth Economic Rehabilitation and RecoveryGrant|Douglas M. Addison|
|SL-Fourth Economic Rehabilitation and RecoveryGrant|Douglas M. Addison|
|Monitoring& EvaluatingCPRGS Implementation in Vietnam Rural|SeNguyen The Dung|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|RAILWAYS REFORM|George A. Banjo|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
|Sustainable and Equitable Growth Technical Assistance Program|Rogelio C. Marchetti|
When we inspect the relation between the number of children in the eligible age brackets with the mean transfer received by the households (Table 6), we notice that the amounts of transfer received by the households are mostly in line with the allocation rule. Apart from the group without children, the mean transfers are within the bounds of the foreseen transfer.

|Child up to 15|Teenager 16 to 17|Mean transfer|Eligible Transfer|Obs.|
|---|---|---|---|---|
|0|0|69.56|62|3841|
|1|0|74.2|20-82|2904|
|2|0|86.53|40-203|2567|
|3|0|103.16|60-122|1293|
|0|1|74.3|30-92|481|
|1|1|84.54|50-112|575|
|2|1|100.59|70-132|361|
|3|1|117.42|90-152|183|
|0|2|77.41|60-122|32|
|1|2|101.72|80-142|38|
|2|2|113.4|100-162|24|
|3|2|124.83|120-182|14|
|all|all|90.51|20-182|13369|

Table 6: Observed BFP transfers by number of children (irrespective of income)

# **5 Identification strategy and estimation**

Based on our conceptual framework, there are three potential mechanisms through which CCT participation can affect household expenditure decisions. We will briefly review them in turn. If we abstract from the conditionalities and assume exogenous preferences of the unitary household, we would expect to see household expenditures adapt to the expansion of the overall household budget. This income effect constitutes the first mechanism.

In the theoretical discussion, we identified two reasons why the impact of the CCT may go beyond the pure income effect. Different preferences of household members together with a change in bargaining power due to the program can affect household choices. If we assume that women have different preferences than men and the cash transfer shifts bargaining power towards women, we would expect changes in household expenditure decisions due to the described gender channel. This second channel has been previously suggested in the literature (e.g. Attanasio et al. [2011]).

Third, by definition, a Conditional Cash Transfer program is more than an unconditional cash transfer; policy makers posit a (positive) effect of the conditionality on overall welfare. Otherwise, conditionalities which are costly to implement and monitor would be ineffective from a public 27
**Table A2.CR.D: Intergenerational Persistence in Schooling in Rural China - -** **<u>(18 30 Years Age Cohorts. First born Sample)</u>**

|||**LINEAR CEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft|0.25|0.32|0.073|0.2|0.32|0.12|
||(0.022)***|(0.024)***|(0.032)**|(0.062)***|(0.065)***|(0.090)|
||[0.030]***|[0.044]***|[0.031]***|[0.091]**|[0.098]***|[0.055]|
|Quadratic Coefft.||||0.0043|0.00048|-0.0039|
|||||(0.0050)|(0.0052)|(0.0072)|
|||||[0.0063]|[0.0060]|[0.0042]|
|Intercept|8.11|7.43|-0.68|8.16|7.43|-0.73|
||(0.17)***|(0.18)***|(0.25)***|(0.19)***|(0.20)***|(0.28)***|
||[0.25]***|[0.34]***|[0.24]***|[0.30]***|[0.40]***|[0.24]***|
|No. Observations|1732|1888|3620|1732|1888|3620|
|||**Estima**<br>|**tes of Mobility**|**from Quadrati**|**c CEF**<br>||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|8.11|7.43||**_IGRC_**|0.25|0.32|
|**_ES6 _**|9.61|9.35|||||
|**_ES9_**|10.36|10.31|||||
|**_ES16_**|12.11|12.55|||||

Notes: (1) The data used are CFPS 2016 with first-born children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling, (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
The results are interesting. The societies without a dominant religion are found to be significantly less unequal. For others, religion does not matter.<sup>42</sup> Ethnolinguistic fractionalization remains highly significant such that each 10 percent increase in fractionalization raises inequality by 3.3 Gini points. Contrary to what we found for the world as a whole, this impact is not diminished by higher income level (results not shown here).<sup>43</sup> When superimposed on ethnic cleavages there is also religious fragmentation inequality is curiously less. Is it because in that case a _modus vivendi_ must be found unless the country explodes in a civil war and thus is not even included in the sample?<sup>44</sup> Parliamentary system remains pro-inequality.

But would the effect of ethnic fractionalization be reduced if country becomes more democratic? We have seen this to be true for the world as a whole even if this still did not explain away the Africa effect. We test this using formulation **7** . And indeed, within African context, greater democratization (unlike greater income) renders the proinequality effect of ethnic fractionalization insignificant even if democracy alone does not reduce inequality. This is potentially an important finding as it suggests an indirect mechanism whereby democracy leads both to lower inequality<sup>45</sup> and (perhaps) to a change away from politics based on ethnic mobilization.<sup>46</sup> 42 The dummies for Christian Orthodox and Buddhist/Hindu are actually country dummies for Ethiopia and Mauritius since they are the only countries with these religions in Africa.

43 Does ethnic fractionalization makes inequality worse in conditions of point-exports and commodity dependence? No, we interact both variables with the fractionalization variable and none of the interactions and none of the variables is significant (regressions available on request). It seems therefore that the effect of ethnic fractionalization can be separated from the fortuitous presence or not of mineral deposits or wealth in commodities.

44 There are no household surveys while countries are undergoing civil wars.

45 By nullifying the pro-inequality effect of ethnic fractionalization.

46 On a related topic, namely why democracy in Africa might facilitate economic reform see van de Walle (1994).

30