rafmacalaba/datause-encoder-data huggingface dataset:
1999. These negative trends led to a revision of the company’s emergency preparedness plan and refurbishment program, as well the establishment of a new outage management system (OMS).<sup>37</sup>

**Figure 11 System Average Interruption Duration Index (SAIDI)**

<!-- Start of picture text --> 2000<br>1500<br>1000<br>500<br>0<br>97 98 99 00 01 02 03 04<br>year<br>Finland France Hungary<br>Italy Ireland Netherlands<br>Spain Sweden South Africa<br>SAIDI - minutes per year<br><!-- End of picture text --> _Sources_ : Eskom Annual Report (various years) and CEER (2005).<sup>38</sup> In early 2000, the number of blackouts and brownouts began to increase. These problems were largely attributed to the deteriorating condition of the distribution networks and their effective decapitalization due to the decreasing amounts that were budgeted and spent on maintenance by the provincial and local governments. The NER conducted a number of surveys to assess the experience of electricity users and evaluate their interactions with the utilities. It was found that:

- an increasing number of customers were not happy with their supply

- reliability and availability of electricity was the main reason

- customers claimed that the situation was deteriorating over time

- utilities did not give customers the attention they deserved

- some claimed that there was a visible lack of maintenance.

The NER also contacted representative bodies of various customer categories according to the government’s Standard Industrial Classification. The main findings of this survey were:

> 37 Eskom. 1999. _Annual Report_ .

> 38 CEER (2005), _ibid._

24
- 16 -

# error correction formulations.

17. Initially, the error correction formulation of the dynamic model is specified using only the real commodicy price indices and the real interest rate variables 1/. Following the 'general-to-simple' modelling methodology [Hendry (1986)), a parsimonious representation (as few variables as possible) of the data-generating process was obtained (Table 5). The main finding from the estimated dynamic model is that the error correction term Zt is statistically very significant in each of the equations. Tests for serial correlation, normality and out-of-sample forecasting performance indicate strongly that the models have been specified correctly. As a test of their forecasting stability, the equations were also estimated after excluding the last ten periods and used to forecast the last 10 periods. The results are graphed in Annex A with the error bars showing standard errors at 5Z. The forecasting performance of the equations is excellent except in the 1985-87 period, when the actual price of each commodity index is considerably lower than the forecast. This may be due to the weakening of the dollar in this period. This hypothesis is tested later in the paper.

1/ The real interest rate is defined as ln (1 + r/100), where r is the nominal rate. This definition produces a series almost identical to the use of r/(l+inflation).
environment in which firms and governments operated during the 19th century, considering both the nature of risks and the mechanisms used to ameliorate them. The fourth Section descibes the financial arrangements that grew up in response. Evidence is drawn from expeiene with infastructre ivestnents as diverse as turnpikes, canals and docks, tramways, and electrical and lighting systems, though railways command center stage. The railways were the most prominent and capital-intensive infastructure investnents of the 19th century. They forged unified national markets, linked domestc producers to the expanding world economy, facilitted the development of modern high-speed-throughput production tehniques, and provided a hotbed for the adoption of modem management practices. For all these reasons they deserve the promience they have long attracted in histones of economic development3 The ffth and sixth secfions consider two devices used to subsidize infrastructe investmet to attract foreign finance: the govemment guantee and te land gnL They show how these worked to relua the credit-atoning constrait but at the same weakeed the incentive for creditos to monitor managemez± encouaging the lamr to divert resources into unproductive uses. The seventh and eighth sections consider the implications for growth of 19th century infrastructure investments, distinging social from prive returns. The last section draws out the lessons for today's developig countres 2 **Any** attempt to **argue** t importance of raiws must confont _Fogs's_ (1964) conclusion tat the social saving due to rilway constuctin the U.S. wer smimmL Fog.'s figmues consder only socid savings due to fright hage, however, neglecting passeg tffic. And given diff across counties in geography and tpogrphy, wblquet studis have yieded **muhlr etimatos** r **otr** prts of the world Nor do soial sv callations attempt to qafy _p_ the dynamic _dffc emphszd_ by athors as diverse - Jeak (1944). Chadler (1965) md Wlliaso (1974).

4
- 5 -

In percentage terms, the trades with Africa and Latin<sup>America are still</sup> negligible. However, indications are that much growth will<sup>occur in these</sup> international market segments over the next few years.

6. While import trades have shown a declining trend as a<sup>proportion</sup> of world import trades, their volume is still significant. The decline can partially be explained by the inward looking<sup>import substitution</sup> policies followed by the country. But there is also<sup>the gigantic domestic</sup> market whose demand needs to be met, which -to some extent-<sup>accounts for</sup> the low export rates, and possibly also explains the<sup>higher import growth</sup> rate. Overall, the growth of domestic consumption exceeded<sup>that of local</sup> production.

# Main Sources of India's Imports (1986)

Percent Share EEC countries 26.5 Non-OPEC developing countries 18.0 OPEC countries 17.0 COMECON countries 11.1 USA 10.6 Japan 9.1 Others 7.7

**Source: The Economist Intelligence Unit, Country Profile India 1987-88**

7. Looking at the import sector, it becomes apparent that there is an increasing share of general cargo, which reached 57 percent of<sup>India's</sup> total imports (in terms of value) in 1986. By 1988,<sup>the shares of</sup> manufactured goods in the export trades, and of general cargo in<sup>the</sup> import trades were both approaching 70 percent of total<sup>trade volumes. As</sup> a final observation: after a prolonged period of relatively<sup>stable annual</sup> trade volumes, India's export and import trades made annual<sup>quantum jumps</sup> from 1986 onward.

# III. THE PHENOMENON OF **CONTAINERIZATION**

8. Why is it so important to single out exports of manufactured goods and general cargo imports? Because in each case<sup>cargo unitization,</sup> and especially containerization offer a significant potential for sizeable efficiency gains in logistics management and reductions<sup>in the</sup> costs of physical distribution management. During the period 1987-1988 the total tonnage of 'containerizable' Indian export and<sup>imoort cargoes</sup> had reached a level of about 16 million. Under the<sup>assum</sup> on that the volumes of these cargDes would show annual growth ra+-- close to that expected for the economy as a whole (i.e. about six ent), one could
inventory method for investment and a similar approach for educational stocks.

All values have been transformed into differences of log values, with the exception of years of education, where differences of levels are used. The stock of education has been divided into splines to test the impact on growth of 0 to 3 years of schooling and the additional effects of more than 3 years of schooling separately.

Seven different proxies for trade and exchange rate policies were collected from different sources to test the statistical relationship between openness and growth. The first, an annual index of trade liberalization for 1960-84, was derived using observations on exchange rate and commercial policies (Source: Papageorgiou, Miclhaely, and Choksi, 1990). Second, an index of trade liberalization for 1978-88, was calculated using country sources on tariffs and nontariff barriers (source: Thomas, Halevi, and Stanton, 1991). The third, a black market premium, measures the deviation of the black market rate from the official exchange rate (source: International Currency Analysis, Inc., various years). The fourth, trade shares, measures the ratio of exports and imports to GDP (source: World Bank data). The fifth, movement toward international prices, was derived from the relative price of a country's tradables, which was computed using current and constant national accounts price indexes (for more details, see Bhalla and Lau (1991) or the discussion above). This variable uses as a benchmarket the relative price of 11
Most surveys in most developing countries with high levels of illiteracy face similar ethical concerns and survey researchers are acutely aware of these concerns. Often there is an expectation of help that underlies a respondent’s consent to the survey. In cases where the survey is collecting relatively benign information, say on demographic characteristics, or consumption patterns, this is perhaps less of an issue, particularly since respondents have usually been subject to such survey questions in the past. But when the survey is trying to obtain information on particularly traumatic events, the question of what constitutes consent and the ethics of the obligation to intervene and help becomes particularly acute.

Collecting information on issues that are private, shameful, or imbedded within struggles for power in the surveyed population potentially violates an even more serious ethical principle than informed consent: the principle of “do no harm.” Despite all our efforts to keep the interviews private, it was impossible to keep the nature of our questions a secret. When outsiders come to a village they are objects of curiosity, and people wonder what they are doing there. This can have serious consequences for respondents.

After completing the survey interviews the team of social workers started conducted focus group discussions with the female respondents. One of these was held in the home of an older woman whom the survey team had befriended. A group of about 8 women had gathered for the discussion at about 11 in the morning. Given the dense clustering of homes in the village we could not keep the location of the group discussion a secret. About half an hour into the discussion a man burst in to the room (the door was unlocked) and furiously walked over to one of the women and hit her in the head screaming “get back and do your work you -----, how dare you ignore your children. You should be at home cooking, not talking to these people.” He then grabbed her right ear and pulled her out the room. We knew from our interview with her that the 9
# Outward and Upward Construction: A 3D Analysis of the Global Building Stock

Thomas Esch (DLR-DFD) Klaus Deininger (World Bank) Remi Jedwab (GWU)<sup>*</sup> Daniela Palacios-Lopez (DLR-DFD) Keywords: World Built Environment in 3D; Construction; Vertical and Horizontal Expansion; Global Socio-Economic Development; Urbanization; Housing; Poverty; Sustainability

> *Corresponding author: Remi Jedwab: George Washington University, D.C., USA. jedwab@gwu.edu. Thomas Esch: German Remote Sensing Data Center (DFD), German Aerospace Center (DLR); Oberpfaffenhofen, Germany. Klaus Deininger: Development Research Group, The World Bank; Washington D.C., USA. Daniela Palacios-Lopez: German Remote Sensing Data Center (DFD), German Aerospace Center (DLR); Oberpfaffenhofen, Germany. The authors thank Jampel Dell’Angelo and two anonymous referees for their comments. The authors acknowledge the contributions of Julian Zeidler, Mattia Marconcini and Elisabeth Brzoska (German Aerospace Center). Funding: European Space Agency [contract no. 4000126100/19/I-EF]. EU-funded ACP-EU Natural Disaster Risk Reduction Program, managed by the Global Facility for Disaster Reduction and Recovery of the World Bank [contract no. 7194331, contract no. 7196541]. World Bank Multi-Donor Trust Fund (TF055155) on Land Policies for Growth and Poverty Reduction. R. Jedwab is grateful to Andrew Durrer, Eleanor Greenbaum, Claudia Groeling and Madison Fallon for research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely ours. 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 or the governments they represent.
5 reproduce the model of Davoodi and Zou, on decentralization and growth, using their own measures of decentralization and find that tax autonomy and non-tax autonomy have a positive correlation with economic growth, while tax sharing has a negative one. Since in most developing economies tax sharing and grants are the main instruments of decentralization, this may explain the findings of Davoodi and Zou (1998). These results of Ebel and Yilmaz suggest that the level of subnational governments’ control over their revenues can influence economic performance.

Ebel and Yilmaz (2002) also replicate the model of Oates with their own indicators of decentralization. They find that fiscal autonomy lead to a smaller state while fiscal dependency and tax sharing had no significant impact. They also find that non-tax autonomy had a positive impact on the public sector size, which makes the interpretation of their results difficult. It should be mentioned though that the transition economies included in their analysis had gone through a major restructuring in the past decade (Bird and Banta, 1999), which can explain in part their mixed results.

In the Ebel and Yilmaz study, no variable of total decentralization has been computed. Ratios of different subnational revenue sources over total revenue are the only independent variables. Also, no attention has been given to the size of subnational governments. The subnational share of government revenue or expenditure is not part of any measure, leaving aside this important aspect of fiscal decentralization.

# **II - Econometric models and data sources**

# _The OECD data on government finance_

We now present the data from the OECD surveys (2001, 2002) on 10 European transition countries (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic and Slovenia).
is no effect of the information intervention on pension participation for the total sample.<sup>23</sup>

# **4.3 Assessing the Potential for Bias Due to Attrition**

Due to the mobile nature of the migrant population, one observes considerable attrition between RUMiC waves. Between 2015 and 2016 the sample attrition rate was 28.3%, thus it is reasonable to consider whether attrition might affect both randomization and the interpretation of our results. To examine this question, we first compare Panels A and B of Table 1. From the table, note that most individual characteristics are balanced for the total sample as well as for the panel respondents, hence, they should also be balanced for those who attrit after the 2015 wave. The imbalances in firm-level control variables and in city dummy variables across control and treatment groups are quite similar for both the total sample and the stayers, suggesting no significant variation in imbalances between stayers and attritors.

Although we do not observe a difference in the balance test between the attritors and the stayers, attrition may still affect our randomization in unobserved ways. To examine the extent to which this is the case, we first employ a Heckman sample selection correction model to control for attrition bias. More specifically, we estimate an attrition equation. For equations (2) and (3), the sample selection models are specified as: _and_ where **Z** is a set of instruments identifying attrition and all the other variables are the same as in equations (2) and (3). The results obtained from equation (5) are then used to calculate an Inverse-Mills ratio, which, in turn, is included in the estimation of equation (2) to control for potential sample selection bias. The instruments used in equation (5) follow a strategy of using respondent baseline attitudes toward the survey to predict attrition (e.g. Mu, 2006) and are drawn from two questions answered by enumerators in the 2015 survey. The first question is “Do you think the respondent was careful and serious in answering the questions? 1. very serious throughout the survey; 2. fair; 3. not very serious.” An indicator variable is set equal to one if the enumerator reported that the respondent was “very serious throughout the survey” or “fair.” The second question asked “To what extent do you think the respondent’s answers are reliable? 1. very reliable; 2. relatively reliable; 3. fair; 4. relatively not reliable; and 5. not reliable.” For this instrument, an indicator variable is set equal to one if the response was ‘very reliable’, ‘relatively reliable’ or ‘fair,’ and zero

> 23The results are available upon request from the authors.

20
with the hypothesis that property owners who had been more exposed to prior enforcement activity by the LRA were more responsive to the enforcement message.

In the absence of direct information on the different enforcement activities carried out by area, the billing and payment records provide some suggestive evidence of the outcome of enforcement efforts across areas. We use the delinquency recovery rate from the prior fiscal year as a proxy for the extent of prior enforcement efforts. Table A6, column 4, shows that for properties similar to the experimental sample (tax liability below $1,000 and with tax arrears), the payment rate in area two is 9 percentage points higher than in area one ( _p_ = 0 _._ 0003), and the rate in area three is 5 percentage points higher than area one ( _p_ = 0 _._ 057). This pattern is consistent with a greater effect of the enforcement treatment in area two due to higher credibility of enforcement based on past experience.

Although the LRA did not systematically record follow up enforcement activities carried out against delinquent property owners in the treatment group that did not respond, there were no significant changes to their collection capacity around this time and the available evidence suggests that their actions, if any, were not substantial. We find no impact of the intervention beyond the first year (table 4 and figure 4). This result contrasts with the sustained compliance impact in experiment one, where there was a notable investment in identification capacity and indicates that shifting long-term beliefs about enforcement probability requires real changes in collection capacity.

# **6 Conclusion**

This paper offers a framework to study tax capacity of emerging economies as three different dimensions: identification, detection, and collection capacity. Given the well-established body of evidence on the importance of verifying the amount of tax liabilities, for example, through third-party information, this paper focuses instead on identification and collection. It highlights the importance of first of all identifying taxable entities and tax bases to make the tax base more legible to the state. However, it also underscores that legibility is insufficient and demonstrates the key complementary role of the state’s capacity to enforce tax laws and 22
<!-- Start of picture text --> Figure 6. Poverty and inequality change between income<br>concepts<br><!-- End of picture text --> <!-- Start of picture text --> Figure 7. Change in inequality between fiscal Interventions<br>and previous income concept<br><!-- End of picture text --> <!-- Start of picture text --> 1<br><!-- End of picture text --> <!-- Start of picture text --> 45<br><!-- End of picture text --> <!-- Start of picture text --> Poverty (national)<br><!-- End of picture text --> <!-- Start of picture text --> Inequality (Gini)<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Percent<br><!-- End of picture text --> <!-- Start of picture text --> 0<br><!-- End of picture text --> <!-- Start of picture text -->
-1<br><!-- End of picture text -->

<!-- Start of picture text -->
-2<br><!-- End of picture text -->

<!-- Start of picture text -->
-3<br><!-- End of picture text -->

<!-- Start of picture text -->
-4<br><!-- End of picture text -->

<!-- Start of picture text --> Market<br>Income<br><!-- End of picture text --> <!-- Start of picture text --> Disposable Consumable Final Income<br><!-- End of picture text --> <!-- Start of picture text --> Income<br><!-- End of picture text --> <!-- Start of picture text --> Income<br><!-- End of picture text --> Note: Poverty is measured at the national poverty line. Inequality is measured by the Gini Index. Market income includes all income from wages and earnings, capital incomes and rents, and private remittances. Disposable income subtracts direct taxes and adds direct transfers. Consumable income subtracts indirect taxes and adds indirect subsidies. Final income adds in-kind health and education spending.

**_Table 3. Fiscal Loss or Gain at Consumable Income by Poverty Line_**

||**Fiscal Impoverishment**|**Fiscal Gains**|
|---|---|---|
||Post-fiscal losers<br>(percent total<br>population)|Post-fiscal gainers<br>(percent total<br>population)|
|_At consumable income_|||
|PPP $1.9|0.0|0.7|
|PPP $3.2|0.2|2.6|
|PPP $5.5|3.1|12.7|
|Nationalpovertyline|4.2|14.7|

Note: The table shows loss and gains based on households’ consumable income, which is defined as market income – direct and indirect tax payments + direct transfers. It excludes in-kind transfers (for education or health care). PPP = purchasing power parity, in 2011 U.S. dollars. a. “Fiscal impoverishment” is the extent to which fiscal system (of taxes and transfers) either (a) make a substantial portion of the poor even poorer, of (b) make the nonpoor poor. In other words, if the poor pay more in taxes than they receive in transfers, they are fiscally impoverished (Higgins and Lustig 2016). b. “Fiscal gains to the poor” is the opposite of fiscal impoverishment, representing more in gains from transfers than they pay in taxes (Higgins and Lustig 2016).